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1
Md., Abu Marjan, Rashedul Islam Md., Palash Uddin Md., Ibn Afjal Masud, and Al Mamun Md. "PCA-based dimensionality reduction for face recognition." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 5 (2021): 1622–29. https://doi.org/10.12928/telkomnika.v19i5.19566.
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In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and discuss the mostly used statistical DR technique called principal component analysis (PCA) in detail with a view to addressing the classical face recognition problem. Therefore, we, more devotedly, propose a solution to either a typical face or individual face recognition based on the principal components, which are constructed using PCA on the face images. We simulate the proposed solution with several training and test sets of manually captured face images and also with the popular Olivetti Research Laboratory (ORL) and Yale face databases. The performance measure of the proposed face recognizer signifies its superiority.
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QIU, XIAN'EN, ZHONG ZHAO, GUOCAN FENG, and PATRICK S. P. WANG. "A GENERAL FRAMEWORK FOR MANIFOLD RECONSTRUCTION FROM DIMENSIONALITY REDUCTION." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 02 (2014): 1451001. http://dx.doi.org/10.1142/s021800141451001x.
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Recently, many dimensionality reduction (DR) algorithms have been developed, which are successfully applied to feature extraction and representation in pattern classification. However, many applications need to re-project the features to the original space. Unfortunately, most DR algorithms cannot perform reconstruction. Based on the manifold assumption, this paper proposes a General Manifold Reconstruction Framework (GMRF) to perform the reconstruction of the original data from the low dimensional DR results. Comparing with the existing reconstruction algorithms, the framework has two significant advantages. First, the proposed framework is independent of DR algorithm. That is to say, no matter what DR algorithm is used, the framework can recover the structure of the original data from the DR results. Second, the framework is space saving, which means it does not need to store any training sample after training. The storage space GMRF needed for reconstruction is far less than that of the training samples. Experiments on different dataset demonstrate that the framework performs well in the reconstruction.
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Amit R Pathare. "Dimensionality Reduction of Hyperspectral Images Using Pixel Block Distance for Efficient Classification." Advances in Nonlinear Variational Inequalities 27, no. 4 (2024): 367–77. http://dx.doi.org/10.52783/anvi.v27.1603.
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Hyperspectral imaging provides useful information in the field of satellite imaging. The extensive spectral and spatial data contained within hyperspectral images (HSI) needs to be analyzed for retrieving the insights about the geographical details. The redundancy in spectral bands results in high dimensionality. The high dimensionality of HSI results in increased computational complexity. This subsequently influences the classification accuracy. Consequently, dimensionality reduction (DR) of HSI is essential before the classification. State-of-the-art DR techniques fail to recognize the nonlinearity in hyperspectral data. The proposed distance measure utilizes information derived from both spatial and spectral domains. The existing DR methods can be included with the proposed distance measure to address the issue of nonlinearity in HSI data. The tests are carried out on DR methods included with the proposed distance measure to assess the results of classification utilizing Support Vector Machine (SVM). Results of classification show that DR techniques incorporated with proposed measures address the nonlinearity present in HSI data.
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Manikandan, Deepa, and Jayaseelan Dhilipan. "Machine learning approach for intrusion detection system using dimensionality reduction." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 430. http://dx.doi.org/10.11591/ijeecs.v34.i1.pp430-440.
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As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.
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Manikandan, Deepa, and Jayaseelan Dhilipan. "Machine learning approach for intrusion detection system using dimensionality reduction." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 1 (2024): 430–40. https://doi.org/10.11591/ijeecs.v34.i1.pp430-440.
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As cyberspace has emerged, security in all the domains like networks, cloud, and databases has become a greater concern in real-time distributed systems. Existing systems for detecting intrusions (IDS) are having challenges coping with constantly changing threats. The proposed model, DR-DBMS (dimensionality reduction in database management systems), creates a unique strategy that combines supervised machine learning algorithms, dimensionality reduction approaches and advanced rule-based classifiers to improve intrusion detection accuracy in terms of different types of attacks. According to simulation results, the DR-DBMS system detected the intrusion attack in 0.07 seconds and with a smaller number of features using the dimensionality reduction and feature selection techniques efficiently.
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Shen, Xingchen, Shixu Fang, and Wenwen Qiang. "Dimensionality Reduction by Similarity Distance-Based Hypergraph Embedding." Atmosphere 13, no. 9 (2022): 1449. http://dx.doi.org/10.3390/atmos13091449.
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Dimensionality reduction (DR) is an essential pre-processing step for hyperspectral image processing and analysis. However, the complex relationship among several sample clusters, which reveals more intrinsic information about samples but cannot be reflected through a simple graph or Euclidean distance, is worth paying attention to. For this purpose, we propose a novel similarity distance-based hypergraph embedding method (SDHE) for hyperspectral images DR. Unlike conventional graph embedding-based methods that only consider the affinity between two samples, SDHE takes advantage of hypergraph embedding to describe the complex sample relationships in high order. Besides, we propose a novel similarity distance instead of Euclidean distance to measure the affinity between samples for the reason that the similarity distance not only discovers the complicated geometrical structure information but also makes use of the local distribution information. Finally, based on the similarity distance, SDHE aims to find the optimal projection that can preserve the local distribution information of sample sets in a low-dimensional subspace. The experimental results in three hyperspectral image data sets demonstrate that our SDHE acquires more efficient performance than other state-of-the-art DR methods, which improve by at least 2% on average.
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K., B. V. Brahma Rao, Krishnam Raju Indukuri R, Suresh Varma P., and V. Rama Sundari M. "Evaluation of Various DR Techniques in Massive Patient Datasets using HDFS." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 1–6. https://doi.org/10.35940/ijrte.D6508.1110421.
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The objective of comparing various dimensionality techniques is to reduce feature sets in order to group attributes effectively with less computational processing time and utilization of memory. The various reduction algorithms can decrease the dimensionality of dataset consisting of a huge number of interrelated variables, while retaining the dissimilarity present in the dataset as much as possible. In this paper we use, Standard Deviation, Variance, Principal Component Analysis, Linear Discriminant Analysis, Factor Analysis, Positive Region, Information Entropy and Independent Component Analysis reduction algorithms using Hadoop Distributed File System for massive patient datasets to achieve lossless data reduction and to acquire required knowledge. The experimental results demonstrate that the ICA technique can efficiently operate on massive datasets eliminates irrelevant data without loss of accuracy, reduces storage space for the data and also the computation time compared to other techniques.
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Li, Pei Heng, Taeho Lee, and Hee Yong Youn. "Dimensionality Reduction with Sparse Locality for Principal Component Analysis." Mathematical Problems in Engineering 2020 (May 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/9723279.
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Various dimensionality reduction (DR) schemes have been developed for projecting high-dimensional data into low-dimensional representation. The existing schemes usually preserve either only the global structure or local structure of the original data, but not both. To resolve this issue, a scheme called sparse locality for principal component analysis (SLPCA) is proposed. In order to effectively consider the trade-off between the complexity and efficiency, a robust L2,p-norm-based principal component analysis (R2P-PCA) is introduced for global DR, while sparse representation-based locality preserving projection (SR-LPP) is used for local DR. Sparse representation is also employed to construct the weighted matrix of the samples. Being parameter-free, this allows the construction of an intrinsic graph more robust against the noise. In addition, simultaneous learning of projection matrix and sparse similarity matrix is possible. Experimental results demonstrate that the proposed scheme consistently outperforms the existing schemes in terms of clustering accuracy and data reconstruction error.
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Tangkaratt, Voot, Ning Xie, and Masashi Sugiyama. "Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization." Neural Computation 27, no. 1 (2015): 228–54. http://dx.doi.org/10.1162/neco_a_00683.
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Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroskedastic, and asymmetric. In such a case, estimating the conditional density itself is preferable, but conditional density estimation (CDE) is challenging in high-dimensional space. A naive approach to coping with high dimensionality is to first perform dimensionality reduction (DR) and then execute CDE. However, a two-step process does not perform well in practice because the error incurred in the first DR step can be magnified in the second CDE step. In this letter, we propose a novel single-shot procedure that performs CDE and DR simultaneously in an integrated way. Our key idea is to formulate DR as the problem of minimizing a squared-loss variant of conditional entropy, and this is solved using CDE. Thus, an additional CDE step is not needed after DR. We demonstrate the usefulness of the proposed method through extensive experiments on various data sets, including humanoid robot transition and computer art.
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Millstein, Joshua, Francesca Battaglin, Malcolm Barrett, et al. "Partition: a surjective mapping approach for dimensionality reduction." Bioinformatics 36, no. 3 (2019): 676–81. http://dx.doi.org/10.1093/bioinformatics/btz661.
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Abstract Motivation Large amounts of information generated by genomic technologies are accompanied by statistical and computational challenges due to redundancy, badly behaved data and noise. Dimensionality reduction (DR) methods have been developed to mitigate these challenges. However, many approaches are not scalable to large dimensions or result in excessive information loss. Results The proposed approach partitions data into subsets of related features and summarizes each into one and only one new feature, thus defining a surjective mapping. A constraint on information loss determines the size of the reduced dataset. Simulation studies demonstrate that when multiple related features are associated with a response, this approach can substantially increase the number of true associations detected as compared to principal components analysis, non-negative matrix factorization or no DR. This increase in true discoveries is explained both by a reduced multiple-testing challenge and a reduction in extraneous noise. In an application to real data collected from metastatic colorectal cancer tumors, more associations between gene expression features and progression free survival and response to treatment were detected in the reduced than in the full untransformed dataset. Availability and implementation Freely available R package from CRAN, https://cran.r-project.org/package=partition. Supplementary information Supplementary data are available at Bioinformatics online.
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Zhang, Lan, Hongjun Su, and Jingwei Shen. "Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis." Remote Sensing 11, no. 10 (2019): 1219. http://dx.doi.org/10.3390/rs11101219.
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Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA) on each homogeneous region is proposed to fully utilize the KPCA’s ability to acquire nonlinear features. Moreover, for the proposed method, the differences in the DR results obtained based on different fundamental images (the first principal components obtained by principal component analysis (PCA), KPCA, and minimum noise fraction (MNF)) are compared. Extensive experiments show that when 5, 10, 20, and 30 samples from each class are selected, for the Indian Pines, Pavia University, and Salinas datasets: (1) when the most suitable fundamental image is selected, the classification accuracy obtained by SuperKPCA can be increased by 0.06%–0.74%, 3.88%–4.37%, and 0.39%–4.85%, respectively, when compared with SuperPCA, which performs PCA on each homogeneous region; (2) the DR results obtained based on different first principal components are different and complementary. By fusing the multiscale classification results obtained based on different first principal components, the classification accuracy can be increased by 0.54%–2.68%, 0.12%–1.10%, and 0.01%–0.08%, respectively, when compared with the method based only on the most suitable fundamental image.
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Priyadarshini, K. Nivedita, V. Sivashankari, Sulochana Shekhar, and K. Balasubramani. "Comparison and Evaluation of Dimensionality Reduction Techniques for Hyperspectral Data Analysis." Proceedings 24, no. 1 (2019): 6. http://dx.doi.org/10.3390/iecg2019-06209.
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Hyperspectral datasets provide explicit ground covers with hundreds of bands. Filtering contiguous hyperspectral datasets potentially discriminates surface features. Therefore, in this study, a number of spectral bands are minimized without losing original information through a process known as dimensionality reduction (DR). Redundant bands portray the fact that neighboring bands are highly correlated, sharing similar information. The benefits of utilizing dimensionality reduction include the ability to slacken the complexity of data during processing and transform original data to remove the correlation among bands. In this paper, two DR methods, principal component analysis (PCA) and minimum noise fraction (MNF), are applied to the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) dataset of Kalaburagi for discussion.
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Leelavathi, Kandasamy Subramaniam, and Marimuthu Rajasenathipathi. "A Novel Crop Yield Prediction Using Deep Learning and Dimensionality Reduction." International Research Journal of Multidisciplinary Scope 05, no. 01 (2024): 101–12. http://dx.doi.org/10.47857/irjms.2024.v05i01.0158.
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Crop yield prediction (CYP) at the field level is crucial in quantitative and economic assessment for creating agricultural commodities plans for import-export strategies and enhancing farmer incomes. Crop breeding has always required a significant amount of time and money. CYP is developed to forecast higher crop production. This paper proposes an efficient deep learning (DL) and dimensionality reduction (DR) approaches for CYP for Indian regional crops. This paper comprised ‘3’ phases: preprocessing, DR, and classification. Initially, the agricultural data of the south Indian region are collected from the dataset. Then preprocessing is applied to the collected dataset by performing data cleaning and normalization. After that, the DR is performed using squared exponential kernel-based principal component analysis (SEKPCA). Finally, CYP is based on a weight-tuned deep convolutional neural network (WTDCNN), which predicts the high crop yield profit. The simulation outcomes shows that the proposed method attains superior performance for CYP compared to exiting schemes with an improved accuracy of 98.96%.
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Thrun, Michael C., Julian Märte, and Quirin Stier. "Analyzing Quality Measurements for Dimensionality Reduction." Machine Learning and Knowledge Extraction 5, no. 3 (2023): 1076–118. http://dx.doi.org/10.3390/make5030056.
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Dimensionality reduction methods can be used to project high-dimensional data into low-dimensional space. If the output space is restricted to two dimensions, the result is a scatter plot whose goal is to present insightful visualizations of distance- and density-based structures. The topological invariance of dimension indicates that the two-dimensional similarities in the scatter plot cannot coercively represent high-dimensional distances. In praxis, projections of several datasets with distance- and density-based structures show a misleading interpretation of the underlying structures. The examples outline that the evaluation of projections remains essential. Here, 19 unsupervised quality measurements (QM) are grouped into semantic classes with the aid of graph theory. We use three representative benchmark datasets to show that QMs fail to evaluate the projections of straightforward structures when common methods such as Principal Component Analysis (PCA), Uniform Manifold Approximation projection, or t-distributed stochastic neighbor embedding (t-SNE) are applied. This work shows that unsupervised QMs are biased towards assumed underlying structures. Based on insights gained from graph theory, we propose a new quality measurement called the Gabriel Classification Error (GCE). This work demonstrates that GCE can make an unbiased evaluation of projections. The GCE is accessible within the R package DR quality available on CRAN.
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Ko, Sungkyun, and Minho Park. "Efficient Speech Signal Dimensionality Reduction Using Complex-Valued Techniques." Electronics 13, no. 15 (2024): 3046. http://dx.doi.org/10.3390/electronics13153046.
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In this study, we propose the CVMFCC-DR (Complex-Valued Mel-Frequency Cepstral Coefficients Dimensionality Reduction) algorithm as an efficient method for reducing the dimensionality of speech signals. By utilizing the complex-valued MFCC technique, which considers both real and imaginary components, our algorithm enables dimensionality reduction without information loss while decreasing computational costs. The efficacy of the proposed algorithm is validated through experiments which demonstrate its effectiveness in building a speech recognition model using a complex-valued neural network. Additionally, a complex-valued softmax interpretation method for complex numbers is introduced. The experimental results indicate that the approach yields enhanced performance compared to traditional MFCC-based techniques, thereby highlighting its potential in the field of speech recognition.
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Hossain, Md Moazzem, Md Ali Hossain, Abu Saleh Musa Miah, Yuichi Okuyama, Yoichi Tomioka, and Jungpil Shin. "Stochastic Neighbor Embedding Feature-Based Hyperspectral Image Classification Using 3D Convolutional Neural Network." Electronics 12, no. 9 (2023): 2082. http://dx.doi.org/10.3390/electronics12092082.
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The ample amount of information from hyperspectral image (HSI) bands allows the non-destructive detection and recognition of earth objects. However, dimensionality reduction (DR) of hyperspectral images (HSI) is required before classification as the classifier may suffer from the curse of dimensionality. Therefore, dimensionality reduction plays a significant role in HSI data analysis (e.g., effective processing and seamless interpretation). In this article, a sophisticated technique established as t-Distributed Stochastic Neighbor Embedding (tSNE) following the dimension reduction along with a blended CNN was implemented to improve the visualization and characterization of HSI. In the procedure, first, we employed principal component analysis (PCA) to reduce the HSI dimensions and remove non-linear consistency features between the wavelengths to project them to a smaller scale. Then we proposed tSNE to preserve the local and global pixel relationships and check the HSI information visually and experimentally. Lastly, it yielded two-dimensional data, improving the visualization and classification accuracy compared to other standard dimensionality-reduction algorithms. Finally, we employed deep-learning-based CNN to classify the reduced and improved HSI intra- and inter-band relationship-feature vector. The evaluation performance of 95.21% accuracy and 6.2% test loss proved the superiority of the proposed model compared to other state-of-the-art DR reduction algorithms.
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Li, Na, Deyun Zhou, Jiao Shi, Mingyang Zhang, Tao Wu, and Maoguo Gong. "Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction." Remote Sensing 13, no. 4 (2021): 706. http://dx.doi.org/10.3390/rs13040706.
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Due to the superior spatial–spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs). However, most CNN-based methods are supervised while the class labels of HSIs are limited and difficult to obtain. While a few unsupervised CNN-based methods have been proposed recently, they always focus on data reconstruction and are lacking in the exploration of discriminability which is usually the primary goal of DR. To address these issues, we propose a deep fully convolutional embedding network (DFCEN), which not only considers data reconstruction but also introduces the specific learning task of enhancing feature discriminability. DFCEN has an end-to-end symmetric network structure that is the key for unsupervised learning. Moreover, a novel objective function containing two terms—the reconstruction term and the embedding term of a specific task—is established to supervise the learning of DFCEN towards improving the completeness and discriminability of low-dimensional data. In particular, the specific task is designed to explore and preserve relationships among samples in HSIs. Besides, due to the limited training samples, inherent complexity and the presence of noise in HSIs, a preprocessing where a few noise spectral bands are removed is adopted to improve the effectiveness of unsupervised DFCEN. Experimental results on three well-known hyperspectral datasets and two classifiers illustrate that the low dimensional features of DFCEN are highly separable and DFCEN has promising classification performance compared with other DR methods.
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Li, Na, Deyun Zhou, Jiao Shi, Tao Wu, and Maoguo Gong. "Spectral-Locational-Spatial Manifold Learning for Hyperspectral Images Dimensionality Reduction." Remote Sensing 13, no. 14 (2021): 2752. http://dx.doi.org/10.3390/rs13142752.
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Dimensionality reduction (DR) plays an important role in hyperspectral image (HSI) classification. Unsupervised DR (uDR) is more practical due to the difficulty of obtaining class labels and their scarcity for HSIs. However, many existing uDR algorithms lack the comprehensive exploration of spectral-locational-spatial (SLS) information, which is of great significance for uDR in view of the complex intrinsic structure in HSIs. To address this issue, two uDR methods called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE) are proposed. Firstly, to facilitate the extraction of SLS information, a weighted spectral-locational (wSL) datum is generated to break the locality of spatial information extraction. Then, a new SLS distance (SLSD) excavating the SLS relationships among samples is designed to select effective SLS neighbors. In SLSSPP, a new uDR model that includes a SLS adjacency graph based on SLSD and a cluster centroid adjacency graph based on wSL data is proposed, which compresses intraclass samples and approximately separates interclass samples in an unsupervised manner. Meanwhile, in SLSRPE, for preserving the SLS relationship among target pixels and their nearest neighbors, a new SLS reconstruction weight was defined to obtain the more discriminative projection. Experimental results on the Indian Pines, Pavia University and Salinas datasets demonstrate that, through KNN and SVM classifiers with different classification conditions, the classification accuracies of SLSSPP and SLSRPE are approximately 4.88%, 4.15%, 2.51%, and 2.30%, 5.31%, 2.41% higher than that of the state-of-the-art DR algorithms.
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Tawfiq, Tawfiq. "Harnessing Dimensionality Reduction with Neutrosophic Net-RBF Neural Networks for Financial Distress Prediction." International Journal of Neutrosophic Science 24, no. 4 (2024): 39–49. http://dx.doi.org/10.54216/ijns.240402.
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Neutrosophy is the study of neutralities and extends the discussion of the truth of opinions. Neutrosophic logic may be employed in any domain, for providing the solution for the ambiguity problems. Several real-time data experience problems such as indeterminacy, incompleteness, and inconsistency. A fuzzy set provides an uncertain solution, and intuitionistic fuzzy set handles incomplete data, but both fail to manage uncertain data. Before bankruptcy, financial distress is the early stage. Bankruptcies caused by financial problems can be seen in the financial statement of the company. The capability to predict financial problems became a crucial area of research since it provides earlier warning for the company. Moreover, predicting financial problems is advantageous for creditors and investors. In this article, we develop a new Dimensionality Reduction with Neutrosophic Net-RBF Neural Networks (DR-NSRBFNN) technique for FCP process. The DR-NSRBFNN technique concentrates on the predictive modelling of financial distress. In the DR-NSRBFNN technique, two major stages are involved. In the preliminary phase, the high dimensionality features can be reduced by the use of arithmetic optimization algorithm (AOA). In the second phase, the DR-NSRBFNN technique applies the NSRBFNN model to predict financial distress. The performance evaluation of the DR-NSRBFNN technique can be examined using distinct aspects. The widespread study stated the improved performance of the DR-NSRBFNN technique compared to other systems
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Jing, Zhang, and Kang Bao Sheng. "A Novel Medical Freehand Sketch 3D Model Retrieval Method by Dimensionality Reduction and Feature Vector Transformation." Computational and Mathematical Methods in Medicine 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4738391.
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To assist physicians to quickly find the required 3D model from the mass medical model, we propose a novel retrieval method, called DRFVT, which combines the characteristics of dimensionality reduction (DR) and feature vector transformation (FVT) method. The DR method reduces the dimensionality of feature vector; only the topMlow frequency Discrete Fourier Transform coefficients are retained. The FVT method does the transformation of the original feature vector and generates a new feature vector to solve the problem of noise sensitivity. The experiment results demonstrate that the DRFVT method achieves more effective and efficient retrieval results than other proposed methods.
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Huang, Hong, Meili Chen, and Yule Duan. "Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding." Remote Sensing 11, no. 9 (2019): 1039. http://dx.doi.org/10.3390/rs11091039.
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Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.
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Wani, Aasim Ayaz. "Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions." PeerJ Computer Science 11 (July 10, 2025): e3025. https://doi.org/10.7717/peerj-cs.3025.
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Dimensionality reduction (DR) simplifies complex data from genomics, imaging, sensors, and language into interpretable forms that support visualization, clustering, and modeling. Yet widely used methods like principal component analysis, t-distributed stochastic neighbor embedding, uniform manifold approximation and projection, and autoencoders are often applied as “black boxes,” neglecting interpretability, fairness, stability, and privacy. This review introduces a unified classification—linear, nonlinear, hybrid, and ensemble approaches—and assesses them against eight core challenges: dimensionality selection, overfitting, instability, noise sensitivity, bias, scalability, privacy risks, and ethical compliance. We outline solutions such as intrinsic dimensionality estimation, robust neighborhood graphs, fairness-aware embeddings, scalable algorithms, and automated tuning. Drawing on case studies from bioinformatics, vision, language, and Internet of Things analytics, we offer a practical roadmap for deploying dimensionality reduction methods that are scalable, interpretable, and ethically sound—advancing responsible artificial intelligence in high-stakes applications.
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YANG, BO, and SONGCAN CHEN. "DISGUISED DISCRIMINATION OF LOCALITY-BASED UNSUPERVISED DIMENSIONALITY REDUCTION." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 07 (2010): 1011–25. http://dx.doi.org/10.1142/s0218001410008275.
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Many locality-based unsupervised dimensionality reduction (DR) algorithms have recently been proposed and demonstrated to be effective to a certain degree in some classification tasks. In this paper, we aim to show that: (1) a discriminant disposal is intentionally or unintentionally induced from the construction of locality in these unsupervised algorithms, however, such a discrimination is often inconsistent with the actual class information, so here called disguised discrimination; (2) sensitivities of these algorithms to local neighbor parameters stem from the inconsistency between the disguised discrimination and the actual class information; (3) how such inconsistency impacts the classification performance of these algorithms. The experiments on the benchmark face datasets testify our statements that are expected to provide some insight into the unsupervised leaning based on locality.
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Sumet Mehta. "Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction." Lahore Garrison University Research Journal of Computer Science and Information Technology 4, no. 4 (2020): 55–72. http://dx.doi.org/10.54692/lgurjcsit.2020.0404109.
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In this paper, we propose a new dimension reduction (DR) algorithm called ensemble graph-based locality preserving projections (EGLPP); to overcome the neighborhood size k sensitivity in locally preserving projections (LPP). EGLPP constructs a homogeneous ensemble of adjacency graphs by varying neighborhood size k and finally uses the integrated embedded graph to optimize the low-dimensional projections. Furthermore, to appropriately handle the intrinsic geometrical structure of the multi-view data and overcome the dimensionality curse, we propose a generalized multi-manifold graph ensemble embedding framework (MLGEE). MLGEE aims to utilize multi-manifold graphs for the adjacency estimation with automatically weight each manifold to derive the integrated heterogeneous graph. Experimental results on various computer vision databases verify the effectiveness of proposed EGLPP and MLGEE over existing comparative DR methods.
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Gao, Wenxu, Zhengming Ma, Weichao Gan, and Shuyu Liu. "Dimensionality Reduction of SPD Data Based on Riemannian Manifold Tangent Spaces and Isometry." Entropy 23, no. 9 (2021): 1117. http://dx.doi.org/10.3390/e23091117.
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Symmetric positive definite (SPD) data have become a hot topic in machine learning. Instead of a linear Euclidean space, SPD data generally lie on a nonlinear Riemannian manifold. To get over the problems caused by the high data dimensionality, dimensionality reduction (DR) is a key subject for SPD data, where bilinear transformation plays a vital role. Because linear operations are not supported in nonlinear spaces such as Riemannian manifolds, directly performing Euclidean DR methods on SPD matrices is inadequate and difficult in complex models and optimization. An SPD data DR method based on Riemannian manifold tangent spaces and global isometry (RMTSISOM-SPDDR) is proposed in this research. The main contributions are listed: (1) Any Riemannian manifold tangent space is a Hilbert space isomorphic to a Euclidean space. Particularly for SPD manifolds, tangent spaces consist of symmetric matrices, which can greatly preserve the form and attributes of original SPD data. For this reason, RMTSISOM-SPDDR transfers the bilinear transformation from manifolds to tangent spaces. (2) By log transformation, original SPD data are mapped to the tangent space at the identity matrix under the affine invariant Riemannian metric (AIRM). In this way, the geodesic distance between original data and the identity matrix is equal to the Euclidean distance between corresponding tangent vector and the origin. (3) The bilinear transformation is further determined by the isometric criterion guaranteeing the geodesic distance on high-dimensional SPD manifold as close as possible to the Euclidean distance in the tangent space of low-dimensional SPD manifold. Then, we use it for the DR of original SPD data. Experiments on five commonly used datasets show that RMTSISOM-SPDDR is superior to five advanced SPD data DR algorithms.
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Dabiri, Zahra, and Stefan Lang. "Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation for Tree Species Classification Using APEX Hyperspectral Imagery." ISPRS International Journal of Geo-Information 7, no. 12 (2018): 488. http://dx.doi.org/10.3390/ijgi7120488.
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Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following main steps: (1) preprocessing (removing noisy bands) and masking out non-forested areas; (2) applying dimensionality reduction techniques, namely, independent component analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel segmentation on the original image and on each of the dimensionality-reduced data cubes; (4) tree species classification using a random forest (RF) classifier; and (5) accuracy assessment. The results revealed that tree species classification using the APEX hyperspectral imagery and DR data cubes yielded good results (with an overall accuracy of 80% for the APEX imagery and an overall accuracy of more than 90% for the DR data cubes). Among the classification results of the DR data cubes, the ICA-transformed components performed best, followed by the MNF-transformed components and the PCA-transformed components. The best class performance (according to producer’s and user’s accuracy) belonged to Picea abies and Salix alba. The other classes (Populus x (hybrid), Alnus incana, Fraxinus excelsior, and Quercus robur) performed differently depending on the different DR data cubes used as the input to the RF classifier.
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Rao, Dr K. B. V. Brahma, Dr R. Krishnam Raju Indukuri, Dr Suresh Varma Penumatsa, and Dr M. V. Rama Sundari. "Evaluation of Various DR Techniques in Massive Patient Datasets using HDFS." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 1–6. http://dx.doi.org/10.35940/ijrte.d6508.1110421.
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The objective of comparing various dimensionality techniques is to reduce feature sets in order to group attributes effectively with less computational processing time and utilization of memory. The various reduction algorithms can decrease the dimensionality of dataset consisting of a huge number of interrelated variables, while retaining the dissimilarity present in the dataset as much as possible. In this paper we use, Standard Deviation, Variance, Principal Component Analysis, Linear Discriminant Analysis, Factor Analysis, Positive Region, Information Entropy and Independent Component Analysis reduction algorithms using Hadoop Distributed File System for massive patient datasets to achieve lossless data reduction and to acquire required knowledge. The experimental results demonstrate that the ICA technique can efficiently operate on massive datasets eliminates irrelevant data without loss of accuracy, reduces storage space for the data and also the computation time compared to other techniques.
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Li, Bin, Wei Pang, Yuhao Liu, et al. "Building Recognition on Subregion’s Multiscale Gist Feature Extraction and Corresponding Columns Information Based Dimensionality Reduction." Journal of Applied Mathematics 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/898705.
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In this paper, we proposed a new building recognition method named subregion’s multiscale gist feature (SM-gist) extraction and corresponding columns information based dimensionality reduction (CCI-DR). Our proposed building recognition method is presented as a two-stage model: in the first stage, a building image is divided into 4 × 5 subregions, and gist vectors are extracted from these regions individually. Then, we combine these gist vectors into a matrix with relatively high dimensions. In the second stage, we proposed CCI-DR to project the high dimensional manifold matrix to low dimensional subspace. Compared with the previous building recognition method the advantages of our proposed method are that (1) gist features extracted by SM-gist have the ability to adapt to nonuniform illumination and that (2) CCI-DR can address the limitation of traditional dimensionality reduction methods, which convert gist matrices into vectors and thus mix the corresponding gist vectors from different feature maps. Our building recognition method is evaluated on the Sheffield buildings database, and experiments show that our method can achieve satisfactory performance.
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Wang, Lanmei, Le Yang, Guibao Wang, Zhihai Chen, and Minggao Zou. "Uni-Vector-Sensor Dimensionality Reduction MUSIC Algorithm for DOA and Polarization Estimation." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/682472.
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This paper addresses the problem of multiple signal classification- (MUSIC-) based direction of arrival (DOA) and polarization estimation and proposes a new dimensionality reduction MUSIC (DR-MUSIC) algorithm. Uni-vector-sensor MUSIC algorithm provides estimation for DOA and polarization; accordingly, a four-dimensional peak search is required, which hence incurs vast amount of computation. In the proposed DR-MUSIC method, the signal steering vector is expressed in the product form of arrival angle function matrix and polarization function vector. The MUSIC joint spectrum is converted to the form of Rayleigh-Ritz ratio by using the feature where the 2-norm of polarization function vector is constant. A four-dimensional MUSIC search reduced the dimension to two two-dimensional searches and the amount of computation is greatly decreased. The theoretical analysis and simulation results have verified the effectiveness of the proposed algorithm.
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Shi, Guangyao, Fulin Luo, Yiming Tang, and Yuan Li. "Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding." Remote Sensing 13, no. 7 (2021): 1363. http://dx.doi.org/10.3390/rs13071363.
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Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification.
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Song, Xin, Xinwei Jiang, Junbin Gao, and Zhihua Cai. "Gaussian Process Graph-Based Discriminant Analysis for Hyperspectral Images Classification." Remote Sensing 11, no. 19 (2019): 2288. http://dx.doi.org/10.3390/rs11192288.
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Dimensionality Reduction (DR) models are highly useful for tackling Hyperspectral Images (HSIs) classification tasks. They mainly address two issues: the curse of dimensionality with respect to spectral features, and the limited number of labeled training samples. Among these DR techniques, the Graph-Embedding Discriminant Analysis (GEDA) framework has demonstrated its effectiveness for HSIs feature extraction. However, most of the existing GEDA-based DR methods largely rely on manually tuning the parameters so as to obtain the optimal model, which proves to be troublesome and inefficient. Motivated by the nonparametric Gaussian Process (GP) model, we propose a novel supervised DR algorithm, namely Gaussian Process Graph-based Discriminate Analysis (GPGDA). Our algorithm takes full advantage of the covariance matrix in GP to constructing the graph similarity matrix in GEDA framework. In this way, more superior performance can be provided with the model parameters tuned automatically. Experiments on three real HSIs datasets demonstrate that the proposed GPGDA outperforms some classic and state-of-the-art DR methods.
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Qin, Xianhao, Chunsheng Li, Yingyi Liang, et al. "Robust Bi-Orthogonal Projection Learning: An Enhanced Dimensionality Reduction Method and Its Application in Unsupervised Learning." Electronics 13, no. 24 (2024): 4944. https://doi.org/10.3390/electronics13244944.
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This paper introduces a robust bi-orthogonal projection (RBOP) learning method for dimensionality reduction (DR). The proposed RBOP enhances the flexibility, robustness, and sparsity of the embedding framework, extending beyond traditional DR methods such as principal component analysis (PCA), neighborhood preserving embedding (NPE), and locality preserving projection (LPP). Unlike conventional approaches that rely on a single type of projection, RBOP innovates by employing two types of projections: the “true” projection and the “counterfeit” projection. These projections are crafted to be orthogonal, offering enhanced flexibility for the “true” projection and facilitating more precise data transformation in the process of subspace learning. By utilizing sparse reconstruction, the acquired true projection has the capability to map the data into a low-dimensional subspace while efficiently maintaining sparsity. Observing that the two projections share many similar data structures, the method aims to maintain the similarity structure of the data through distinct reconstruction processes. Additionally, the incorporation of a sparse component allows the method to address noise-corrupted data, compensating for noise during the DR process. Within this framework, a number of new unsupervised DR techniques have been developed, such as RBOP_PCA, RBOP_NPE, and RBO_LPP. Experimental results from both natural and synthetic datasets indicate that these proposed methods surpass existing, well-established DR techniques.
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Wang, Yingfan, Yiyang Sun, Haiyang Huang, and Cynthia Rudin. "Dimension Reduction with Locally Adjusted Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21357–65. https://doi.org/10.1609/aaai.v39i20.35436.
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Dimension reduction (DR) algorithms have proven to be extremely useful for gaining insight into large-scale high-dimensional datasets, particularly finding clusters in transcriptomic data. The initial phase of these DR methods often involves converting the original high-dimensional data into a graph. In this graph, each edge represents the similarity or dissimilarity between pairs of data points. However, this graph is frequently suboptimal due to unreliable high-dimensional distances and the limited information extracted from the high-dimensional data. This problem is exacerbated as the dataset size increases. If we reduce the size of the dataset by selecting points for a specific sections of the embeddings, the clusters observed through DR are more separable since the extracted subgraphs are more reliable. In this paper, we introduce LocalMAP, a new dimensionality reduction algorithm that dynamically and locally adjusts the graph to address this challenge. By dynamically extracting subgraphs and updating the graph on-the-fly, LocalMAP is capable of identifying and separating real clusters within the data that other DR methods may overlook or combine. We demonstrate the benefits of LocalMAP through a case study on biological datasets, highlighting its utility in helping users more accurately identify clusters for real-world problems.
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Abdulhammed, Razan, Hassan Musafer, Ali Alessa, Miad Faezipour, and Abdelshakour Abuzneid. "Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection." Electronics 8, no. 3 (2019): 322. http://dx.doi.org/10.3390/electronics8030322.
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The security of networked systems has become a critical universal issue that influences individuals, enterprises and governments. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: (i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and (ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset’s feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6% in multi-class and binary classification. Furthermore, in this paper, we propose a Multi-Class Combined performance metric C o m b i n e d M c with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset.
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Feng, Sheng, Xiaoqiang Hua, and Xiaoqian Zhu. "Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction." Entropy 22, no. 9 (2020): 914. http://dx.doi.org/10.3390/e22090914.
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In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract abundant features, then exploit the advantages of matrix information geometry technique by constructing the high-dimensional features as symmetric positive definite (SPD) matrices. In this case, our task for signal detection becomes a binary classification problem lying on an SPD manifold. Promoting the discrimination of heterogeneous samples through information geometric DR technique that is dedicated to SPD manifold, our proposed detector achieves satisfactory signal detection performance in low SNR cases using the K distribution simulation and the real-life sea clutter data, which can be widely used in the field of signal detection.
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Zebari, Rizgar, Adnan Abdulazeez, Diyar Zeebaree, Dilovan Zebari, and Jwan Saeed. "A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction." Journal of Applied Science and Technology Trends 1, no. 2 (2020): 56–70. http://dx.doi.org/10.38094/jastt1224.
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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.
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Varghese, Jijo, and Dr P. Tamil Selvan. "An Adaptive Firefly Optimization (AFO) with Multi-Kernel SVM (MKSVM) Classification for Big Data Dimensionality Reduction." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 7 (2022): 100–111. http://dx.doi.org/10.17762/ijritcc.v10i7.5595.
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The data's dimensionality had already risen sharply in the last several decades. The "Dimensionality Curse" (DC) is a problem for conventional learning techniques when dealing with "Big Data (BD)" with a higher level of dimensionality. A learning model's performance degrades when there is a numerous range of features present. "Dimensionality Reduction (DR)" approaches are used to solve the DC issue, and the field of "Machine Learning (ML)" research is significant in this regard. It is a prominent procedure to use "Feature Selection (FS)" to reduce dimensions. Improved learning effectiveness such as greater classification precision, cheaper processing costs, and improved model comprehensibility are all typical outcomes of this approach that selects an optimal portion of the original features based on some relevant assessment criteria. An "Adaptive Firefly Optimization (AFO)" technique based on the "Map Reduce (MR)" platform is developed in this research. During the initial phase (mapping stage) the whole large "DataSet (DS)" is first subdivided into blocks of contexts. The AFO technique is then used to choose features from its large DS. In the final phase (reduction stage), every one of the fragmentary findings is combined into a single feature vector. Then the "Multi Kernel Support Vector Machine (MKSVM)" classifier is used as classification in this research to classify the data for appropriate class from the optimal features obtained from AFO for DR purposes. We found that the suggested algorithm AFO combined with MKSVM (AFO-MKSVM) scales very well to high-dimensional DSs which outperforms the existing approach "Linear Discriminant Analysis-Support Vector Machine (LDA-SVM)" in terms of performance. The evaluation metrics such as Information-Ratio for Dimension-Reduction, Accuracy, and Recall, indicate that the AFO-MKSVM method established a better outcome than the LDA-SVM method.
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Chellappan, Dinesh, and Harikumar Rajaguru. "Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data." Biomimetics 8, no. 6 (2023): 503. http://dx.doi.org/10.3390/biomimetics8060503.
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In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier’s performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier’s performance emphasizes the role of feature selection methods.
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Cai, Fen, Miao-Xia Guo, Li-Fang Hong, and Ying-Yi Huang. "Classification of Hyperspectral Images Based on Supervised Sparse Embedded Preserving Projection." Applied Sciences 9, no. 17 (2019): 3583. http://dx.doi.org/10.3390/app9173583.
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Dimensionality reduction is an important research area for hyperspectral remote sensing images due to the redundancy of spectral information. Sparsity preserving projection (SPP) is a dimensionality reduction (DR) algorithm based on the l1-graph, which establishes the relations of samples by sparse representation. However, SPP is an unsupervised algorithm that ignores the label information of samples and the objective function of SPP; instead, it only considers the reconstruction error, which means that the classification effect is constrained. In order to solve this problem, this paper proposes a dimensionality reduction algorithm called the supervised sparse embedded preserving projection (SSEPP) algorithm. SSEPP considers the manifold structure information of samples and makes full use of the label information available in order to enhance the discriminative ability of the projection subspace. While maintaining the sparse reconstruction error, the algorithm also minimizes the error between samples of the same class. Experiments were performed on an Indian Pines hyperspectral dataset and HJ1A-HSI remote sensing images from the Zhangjiang estuary in Southeastern China, respectively. The results show that the proposed method effectively improves its classification accuracy.
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Jijo Varghese and P. Tamil Selvan. "A Novel Clustering and Matrix Based Computation for Big Data Dimensionality Reduction and Classification." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 1 (2023): 238–51. http://dx.doi.org/10.37934/araset.32.1.238251.
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For higher dimensional or "Big Data (BD)" clustering and classification, the dimensions of documents have to be considered. The overhead of classifying methods might also be reduced by resolving the volumetric issue of documents. However, the dimensions of the shortened collection of documents might potentially generate noise and abnormalities. Previous noise and abnormality information removal strategies include several different approaches that have already been established throughout time. To increase classification accuracy, current classifications or new classification methods that has created to conduct classification, must deal with some of the most difficult issues in BD document categorization and clustering. Hence, the goals of this research are derived from the issues that can be solved only by expanding classification accuracy of classifiers. Superior clusters may also be achieved by using effective "Dimensionality Reduction (DR)". As the first step in this research, we introduce a unique DR approach that preserves word frequency in the document collection, allowing the classification algorithm to obtain improved (or) at least equal classification levels of accuracy with a lower dimensionality set of documents. When clustering "Word Patterns (WPs)" during "WP Clustering (WPC)", we imply a new WP "Similarity Function (SF)" for "Similarity Computation (SC)" to be used as part of WPC. DR of the document collection is accomplished with the use of information gained from various WP clusters. Finally, we provide "Similarity Measures" for SC of high dimensional texts and deliver SF for document classification and deliver SF for document classification. With assessment criteria like "Information-Ratio for Dimension-Reduction", "Accuracy", and "Recall", we discovered that the proposed method WP paired with SC (WP-SC) scaled extremely effectively to higher dimensional "Dataset’s (DS)" and surpasses the current technique AFO-MKSVM. According to the findings, the WP-SC approach produced more favorable outcomes than the LDA-SVM and AFO-MKSVM approaches.
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Venkatesh, B., and J. Anuradha. "A Review of Feature Selection and Its Methods." Cybernetics and Information Technologies 19, no. 1 (2019): 3–26. http://dx.doi.org/10.2478/cait-2019-0001.
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Abstract Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.
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NUMAN, Jamal A. A., and Izham Mohamad YUSOFF. ""The Effect of Dimensionality Reduction on the Real Estate Appraisal Performance Using Tree-Based Machine Learning Models "." Journal of Settlements and Spatial Planning 16, no. 1 (2025): 15–30. https://doi.org/10.24193/jssp.2025.1.02.
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Real estate appraisal is a critical process essential for economic, financial, and business transactions, including buying and selling, mortgage lending, insurance, and property taxation. In this context, model-based real estate appraisal methods face significant challenges such as performance, interpretability, stability, reliability, scalability, flexibility, simplicity, adaptability, applicability, generalizability, comprehensibility, data availability, and evaluation metrics. Among these challenges, performance consistently stands out as a key concern, attracting considerable attention from both academic researchers and industry professionals. With the aim of investigating the effect of dimensionality reduction (DR) on the appraisal performance, three objectives are crafted: identifying the initial features affecting real estate appraisal within Al Bireh city, Palestine, selecting the most influential features, and evaluating model performance when all features are included versus when only the most influential are used employing five statistical metrics. The originality of this research lies in the explicit implementation of DR using multiple feature importance (FI) techniques, multiple models, and multiple evaluation metrics. Specifically, this study includes two FI techniques—namely, inherent FI and Shapley Additive Explanation (SHAP); four models - three tree-based models (decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost)) and a linear regression (LR) model used as a benchmark; and five evaluation metrics: MSE, RMSE, MAE, MAPE, and R². The results indicate no performance improvement when DR is conducted. However, with DR reducing the features from 28 to 6, the relative performance metric decrease is minor, remaining below 5% for all models except LR, and as low as 0.7% in terms of R² for RF, thus concluding the need for a trade-off between the minor decrease in performance and gains in computational efficiency, hardware resources, and data collection. The key implications of DR provide stakeholders with a checklist of key features influencing appraisal value, and increase efficiency by reducing processing time, resources, and data collection.
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Yao, Nan, Feng Qian, and Zuo Lei Sun. "Feature Dimension Reduction and Graph Based Ranking Based Image Classification." Applied Mechanics and Materials 380-384 (August 2013): 4035–38. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.4035.
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Dimensionality reduction (DR) of image features plays an important role in image retrieval and classification tasks. Recently, two types of methods have been proposed to improve both the accuracy and efficiency for the dimensionality reduction problem. One uses Non-negative matrix factorization (NMF) to describe the image distribution on the space of base matrix. Another one for dimension reduction trains a subspace projection matrix to project original data space into some low-dimensional subspaces which have deep architecture, so that the low-dimensional codes would be learned. At the same time, the graph based similarity learning algorithm which tries to exploit contextual information for improving the effectiveness of image rankings is also proposed for image class and retrieval problem. In this paper, after above two methods mentioned are utilized to reduce the high-dimensional features of images respectively, we learn the graph based similarity for the image classification problem. This paper compares the proposed approach with other approaches on an image database.
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Swieboda, Dominika, Jacob Beaver, Erica LaShai Johnson, Ioanna Skountzou, and Rana Chakraborty. "Baby’s First Macrophage: characterizing Hofbauer Cell phenotype through gestation using unsupervised dimensionality reduction." Journal of Immunology 204, no. 1_Supplement (2020): 235.19. http://dx.doi.org/10.4049/jimmunol.204.supp.235.19.
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Abstract Fetal placental macrophages (Hofbauer cells (HCs)) appear at 18 days post-conception and are maintained throughout pregnancy. Despite their identification more than a century ago, there are few studies characterizing the evolution of human HC phenotype through gestation. Using high-dimensional flow cytometry, we measured expression of CD68, CD80, CD86, HLA-DR, CD163, CD206, and CD209 on human HCs ex vivo. This panel includes markers previously validated on term human HCs for immune activation (CD80, CD86) and modulation (DC-SIGN, HLA-DR). To best capture the spectrum of HC diversity across gestation, we employed an unbiased approach to analyze processed datasets from HCs isolated from placentae 12–17 weeks gestational age (n=5), 17–24 weeks gestational age (n=7) and at term (n=5) using t-distributed stochastic neighbor embedding analysis (tSNE). HC populations were more diverse early in pregnancy and at term, compared to the second trimester; this is anticipated given the changing microenvironment following initial placental anchoring to eventual parturition. Marker expression heatmaps were generated to assess gestation-dependent changes in HC phenotype; HCs expressing activation markers were most frequent in early gestation, and reduced by term. HCs bearing markers of immune modulation were most frequent at mid-gestation, and least in early gestation. These results demonstrate the power of utilizing computational methods to analyze high-dimensional flow cytometry data collected from an understudied cell type and demonstrate how HCs phenotypically evolve during human pregnancy.
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Xue, Tianru, Yueming Wang, Yuwei Chen, et al. "Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction." Remote Sensing 13, no. 13 (2021): 2607. http://dx.doi.org/10.3390/rs13132607.
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Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.
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Liu, Hong, Kewen Xia, Tiejun Li, Jie Ma, and Eunice Owoola. "Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding." Sensors 20, no. 16 (2020): 4413. http://dx.doi.org/10.3390/s20164413.
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Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the problem of the curse of dimensionality and improve classification accuracy, an improved spatial–spectral weight manifold embedding (ISS-WME) algorithm, which is based on hyperspectral data with their own manifold structure and local neighbors, is proposed in this study. The manifold structure was constructed using the structural weight matrix and the distance weight matrix. The structural weight matrix was composed of within-class and between-class coefficient representation matrices. These matrices were obtained by using the collaborative representation method. Furthermore, the distance weight matrix integrated the spatial and spectral information of HSIs. The ISS-WME algorithm describes the whole structure of the data by the weight matrix constructed by combining the within-class and between-class matrices and the spatial–spectral information of HSIs, and the nearest neighbor samples of the data are retained without changing when embedding to the low-dimensional space. To verify the classification effect of the ISS-WME algorithm, three classical data sets, namely Indian Pines, Pavia University, and Salinas scene, were subjected to experiments for this paper. Six methods of dimensionality reduction (DR) were used for comparison experiments using different classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The experimental results show that the ISS-WME algorithm can represent the HSI structure better than other methods, and effectively improves the classification accuracy of HSIs.
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Habermann, Mateus, Elcio Hideiti Shiguemori, and Vincent Frémont. "Unsupervised Cluster-Wise Hyperspectral Band Selection for Classification." Remote Sensing 14, no. 21 (2022): 5374. http://dx.doi.org/10.3390/rs14215374.
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A hyperspectral image provides fine details about the scene under analysis, due to its multiple bands. However, the resulting high dimensionality in the feature space may render a classification task unreliable, mainly due to overfitting and the Hughes phenomenon. In order to attenuate such problems, one can resort to dimensionality reduction (DR). Thus, this paper proposes a new DR algorithm, which performs an unsupervised band selection technique following a clustering approach. More specifically, the data set was split into a predefined number of clusters, after which the bands were iteratively selected based on the parameters of a separating hyperplane, which provided the best separation in the feature space, in a one-versus-all scenario. Then, a fine-tuning of the initially selected bands took place based on the separability of clusters. A comparison with five other state-of-the-art frameworks shows that the proposed method achieved the best classification results in 60% of the experiments.
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Sun, Xiao Jing, and Ai Bin Chen. "Improved Multi Scale DR Medical Image Enhancement Algorithm." Applied Mechanics and Materials 530-531 (February 2014): 413–17. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.413.
Full textAbstract:
The original DR image is decomposed into different scale and frequency of the band image sequence by using Laplace gaussian pyramid model methods. Using multi-scale image enhancement algorithm to enhance the High frequency component of the decomposed image, Then adjust the light of the low frequency part to make the reconstructed image illumination contrast more reasonable. The enhanced process according to different frequency layer image feature make the different gain weight for the different frequency layer image characteristics,so different frequency image layer realize respectively noise smoothing, dimensionality reduction and enhance the effect of edge character.The simulation experiments showed that this Image Processing Algorithm effect is very good.
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Ospanov, Anuar, Igor Romanishkin, Tatiana Savelieva, et al. "Optical Differentiation of Brain Tumors Based on Raman Spectroscopy and Cluster Analysis Methods." International Journal of Molecular Sciences 24, no. 19 (2023): 14432. http://dx.doi.org/10.3390/ijms241914432.
Full textAbstract:
In the present study, various combinations of dimensionality reduction methods with data clustering methods for the analysis of biopsy samples of intracranial tumors were investigated. Fresh biopsies of intracranial tumors were studied in the Laboratory of Neurosurgical Anatomy and Preservation of Biological Materials of N.N. Burdenko Neurosurgery Medical Center no later than 4 h after surgery. The spectra of Protoporphyrin IX (Pp IX) fluorescence, diffuse reflectance (DR) and Raman scattering (RS) of biopsy samples were recorded. Diffuse reflectance studies were carried out using a white light source in the visible region. Raman scattering spectra were obtained using a 785 nm laser. Patients diagnosed with meningioma, glioblastoma, oligodendroglioma, and astrocytoma were studied. We used the cluster analysis method to detect natural clusters in the data sample presented in the feature space formed based on the spectrum analysis. For data analysis, four clustering algorithms with eight dimensionality reduction algorithms were considered.
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Ghobadi, Fatemeh, Amir Saman Tayerani Charmchi, and Doosun Kang. "Feature Extraction from Satellite-Derived Hydroclimate Data: Assessing Impacts on Various Neural Networks for Multi-Step Ahead Streamflow Prediction." Sustainability 15, no. 22 (2023): 15761. http://dx.doi.org/10.3390/su152215761.
Full textAbstract:
Enhancing the generalization capability of time-series models for streamflow prediction using dimensionality reduction (DR) techniques remains a major challenge in water resources management (WRM). In this study, we investigated eight DR techniques and their effectiveness in mitigating the curse of dimensionality, which hinders the performance of machine learning (ML) algorithms in the field of WRM. Our study delves into the most non-linear unsupervised representative DR techniques, including principal component analysis (PCA), kernel PCA (KPCA), multi-dimensional scaling (MDS), isometric mapping (ISOMAP), locally linear embedding (LLE), t-distributed stochastic neighbor embedding (t-SNE), Laplacian eigenmaps (LE), and autoencoder (AE), examining their effectiveness in multi-step ahead (MSA) streamflow prediction. In this study, we conducted a conceptual comparison of these techniques. Subsequently, we focused on their performance in four different case studies in the USA. Moreover, we assessed the quality of the transformed feature spaces in terms of the MSA streamflow prediction improvement. Through our investigation, we gained valuable insights into the performance of different DR techniques within linear/dense/convolutional neural network (CNN)/long short-term memory neural network (LSTM) and autoregressive LSTM (AR-LSTM) architectures. This study contributes to a deeper understanding of suitable feature extraction techniques for enhancing the capabilities of the LSTM model in tackling high-dimensional datasets in the realm of WRM.