Relevant bibliographies by topics / Dimensionality reduction (DR)

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Academic literature on the topic 'Dimensionality reduction (DR)'

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Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Dimensionality reduction (DR)"

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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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Dissertations / Theses on the topic "Dimensionality reduction (DR)"

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Hasangjekaj, Rilind, and Sandra Karlsson. "A reusable framework to accelerate the development of visual analytics applications based on dimensionality reduction." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-88625.

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To be able to visualize and analyze multidimensional data sets, the dimensions have to be reduced to two or three, by using dimensionality reducing (DR) methods. The problem is that there is no established foundation for building applications to handle data sets, DR and visualization and therefore the process of doing so is not as efficient as it could be. This thesis suggests a framework as a solution to the problem, and covers the implementation of such a framework. The framework is used to create a prototype application to ensure that it is useful in such a scenario. For further evaluation, a domain expert tested the framework by following the associated instructions, and answered a questionnaire. The answers were positive and contained comments that were used to improve the instructions, and suggestions on how to improve the framework in the future.
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Book chapters on the topic "Dimensionality reduction (DR)"

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Venugopal, Deepak, Max Garzon, Nirman Kumar, Ching-Chi Yang, and Lih-Yuan Deng. "Metaheuristics of DR Methods." In Dimensionality Reduction in Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_10.

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Deng, Lih-Yuan, Max Garzon, and Nirman Kumar. "What Is Dimensionality Reduction (DR)?" In Dimensionality Reduction in Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05371-9_3.

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Wang, Jianzhong. "Fast Algorithms for DR Approximation." In Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27497-8_15.

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Wang, Jianzhong. "Data Models and Structures of Kernels of DR." In Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27497-8_4.

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Giang, Thanh Trung, Thanh Phuong Nguyen, Tran Quoc Vinh Nguyen, and Dang Hung Tran. "fMKL-DR: A Fast Multiple Kernel Learning Framework with Dimensionality Reduction." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75429-1_13.

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Luengo, Silvia, Alberto Jaenal, Francisco A. Moreno, and Javier Gonzalez-Jimenez. "Dimensionality reduction in images for appearance-based camera localization." In XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja), 2022nd ed. Servizo de Publicacións da UDC, 2022. http://dx.doi.org/10.17979/spudc.9788497498418.0721.

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Appearance-based Localization (AL) focuses on estimating the pose of a camera from the information encoded in an image, treated holistically. However, the high-dimensionality of images makes this estimation intractable and some technique of dimensionality Reduction (DR) must be applied. The resulting reduced image representation, though, must keep underlying information about the structure of the scene to be able to infer the camera pose. This work explores the problem of DR in the context of AL, and evaluates four popular methods in two simple cases on a synthetic environment: two linear (PCA and MDS) and two non-linear, also known as Manifold Learning methods (LLE and Isomap). The evaluation is carried out in terms of their capability to generate lower-dimensional embeddings that maintain underlying information that is isometric to the camera poses.
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R., Tamilarasi, and Prabu Sevugan. "Hyperspectral Image Classification Through Machine Learning and Deep Learning Techniques." In Applications of Artificial Intelligence for Smart Technology. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3335-2.ch008.

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Dimensionality reduction for hyperspectral imagery plays a major role in different scientific and technical applications. It enables the identification of multiple urban-related features on the surface of the earth, such as building, highway (road), and other natural and man-made structures. Since manual road detection and satellite imagery extraction is time-consuming and costly, data time and cost-effective solution with limited user interaction will emerge with road and building extraction techniques. Therefore, the need to focus on a deep survey for improving ML techniques for dimensionality reduction (DR) and automated building and road extraction using hyperspectral imagery. The main purpose of this chapter is to identify the state-of-the-art and trends of hyperspectral imaging theories, methodologies, techniques, and applications for dimensional reduction. A different type of ML technique is included such as SVM, ANN, etc. These algorithms can handle high dimensionality and classification data.
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Salihu, Artan, Markus Rupp, and Stefan Schwarz. "Self-Supervised Learning for Wireless Localization." In 5G and 6G Broadband Communication Networks - Challenges, Trends, and Opportunities [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.1003773.

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In this chapter, we provide an overview of several data-driven techniques for wireless localization. We initially discuss shallow dimensionality reduction (DR) approaches and investigate a supervised learning method. Subsequently, we transition into deep metric learning and then place particular emphasis on a transformer-based model and self-supervised learning. We highlight a new research direction of employing designed pretext tasks to train AI models, enabling them to learn compressed channel features useful for wireless localization. We use datasets obtained in massive multiple-input multiple-output (MIMO) systems indoors and outdoors to investigate the performance of the discussed approaches.
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Gerolymatos, Stavros, Xenophon Evangelopoulos, Vladimir V. Gusev, and John Y. Goulermas. "Cluster Exploration Using Informative Manifold Projections." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240717.

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Dimensionality reduction (DR) is one of the key tools for the visual exploration of high-dimensional data and uncovering its cluster structure in two- or three-dimensional spaces. The vast majority of DR methods in the literature do not take into account any prior knowledge a practitioner may have regarding the dataset under consideration. We propose a novel method to generate informative embeddings which not only factor out the structure associated with different kinds of prior knowledge but also aim to reveal any remaining underlying structure. To achieve this, we employ a linear combination of two objectives: firstly, contrastive PCA that discounts the structure associated with the prior information, and secondly, kurtosis projection pursuit which ensures meaningful data separation in the obtained embeddings. We formulate this task as a manifold optimization problem and validate it empirically across a variety of datasets considering three distinct types of prior knowledge. Lastly, we provide an automated framework to perform iterative visual exploration of high-dimensional data.
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Conference papers on the topic "Dimensionality reduction (DR)"

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Marcílio-Jr, Wilson E., and Danilo M. Eler. "Interpretation and Hierarchical methods for Dimensionality Reduction." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sibgrapi.est.2023.27456.

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High-dimensional data analysis is a ubiquitous task in practical and research activities. Dimensionality Reduction (DR) techniques are usually employed as they map highdimensional data to lower spaces and allow for knowledge discovery. This thesis focuses on the interpretability and representation aspects of non-linear DR approaches’ output, such as t-SNE and UMAP. That is, we propose methods for interpreting and hierarchically learning embeddings. To accomplish these goals, the following main research activities were carried out, representing separate but interconnected works: (1) a sampling method in visual space (R2) that can preserve class boundary structures while keeping outliers visible; (2) a technique for understanding cluster formation by leveraging statistical tests on the feature values after dimensionality reduction; (3) we advance the state-of-the-art by adapting SHAP to explain cluster formation after dimensionality reduction; (4) a novel hierarchical DR technique that employs an adaptive kernel for global/local neighborhood learning while preserving context across embeddings.
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Griparis, Andreea, Daniela Faur, and Mihai Datcu. "DR-KNN: A Hybrid Approach for Dimensionality Reduction of EO Image Datasets." In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323633.

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SOLEIMANI-BABAKAMALI, MOHAMMAD HESAM, ISMINI LOURENTZOU, and RODRIGO SARLO. "DOES THE CURSE OF DIMENSIONALITY APPLY TO UNSUPERVISED SHM? INVESTIGATING THE TRADE-OFF BETWEEN LOSS OF INFORMATION AND GENERALIZABILITY TO UNSEEN STRUCTURAL CONDITIONS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36311.

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The curse of dimensionality (CD) brings difficulties in pattern recognition problems, such as those found in structural health monitoring (SHM). Dimensionality reduction techniques (DR) make data more manageable by reducing noise and noninformative portions. There exists a trade-off between CD and the loss of information due to the application of DR. Even though in supervised SHM, DR techniques are shown to be effective, for unsupervised SHM, the trade-off must be assessed due to the unknown data population of novel classes. This study assesses the trade-off concerning a novel method working with a raw frequency-domain feature, the fast Fourier transform (FFT). Different DR techniques are applied to the initial FFT-based feature to assess the trade-off, and detection results are compared. The results indicate that the loss of information can have detrimental effects, such as lowering the detection accuracy by 60% for the autoencoder-based DR. The accuracy reduction is present for all different DR techniques applied in the study; however, regularization lessens the accuracy decrements. This phenomenon indicates the assumption that novelties show themselves in less-vary portions of the baseline condition to be not true.
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Bahri, Maroua, Albert Bifet, Silviu Maniu, and Heitor Murilo Gomes. "Survey on Feature Transformation Techniques for Data Streams." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/668.

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Mining high-dimensional data streams poses a fundamental challenge to machine learning as the presence of high numbers of attributes can remarkably degrade any mining task's performance. In the past several years, dimension reduction (DR) approaches have been successfully applied for different purposes (e.g., visualization). Due to their high-computational costs and numerous passes over large data, these approaches pose a hindrance when processing infinite data streams that are potentially high-dimensional. The latter increases the resource-usage of algorithms that could suffer from the curse of dimensionality. To cope with these issues, some techniques for incremental DR have been proposed. In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms.
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