Journal articles: 'T-distributed Stochastic Neighbour Embedding'

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Rice, Iain. "Γ-stochastic neighbour embedding for feed-forward data visualization". Information Visualization 17, № 4 (2017): 306–15. http://dx.doi.org/10.1177/1473871617715212.

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t-Distributed stochastic neighbour embedding is one of the most popular non-linear dimension-reduction techniques used in multiple application domains. In this article, we propose a variation on the embedding neighbourhood distribution, resulting in Γ-stochastic neighbour embedding, which can construct a feed-forward mapping using a radial basis function network. We compare the visualizations generated by Γ-stochastic neighbour embedding with those of t-distributed stochastic neighbour embedding and provide empirical evidence suggesting the network is capable of robust interpolation and automatic weight regularization.

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Sudha, T., and P. Nagendra Kumar. "Performance Analysis of Dimensionality Reduction Techniques in the Context of Clustering." Asian Journal of Computer Science and Technology 8, S3 (2019): 66–71. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2084.

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Data mining is one of the major areas of research. Clustering is one of the main functionalities of datamining. High dimensionality is one of the main issues of clustering and Dimensionality reduction can be used as a solution to this problem. The present work makes a comparative study of dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis in the context of clustering. High dimensional data have been reduced to low dimensional data using dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis. Cluster analysis has been performed on the high dimensional data as well as the low dimensional data sets obtained through t-distributed stochastic neighbour embedding and Probabilistic principal component analysis with varying number of clusters. Mean squared error; time and space have been considered as parameters for comparison. The results obtained show that time taken to convert the high dimensional data into low dimensional data using probabilistic principal component analysis is higher than the time taken to convert the high dimensional data into low dimensional data using t-distributed stochastic neighbour embedding.The space required by the data set reduced through Probabilistic principal component analysis is less than the storage space required by the data set reduced through t-distributed stochastic neighbour embedding.

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Chan, David M., Roshan Rao, Forrest Huang, and John F. Canny. "GPU accelerated t-distributed stochastic neighbor embedding." Journal of Parallel and Distributed Computing 131 (September 2019): 1–13. http://dx.doi.org/10.1016/j.jpdc.2019.04.008.

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Abimanyu, Satria, Nurdin Bahtiar, and Eko Adi Sarwoko. "Implementasi Metode Support Vector Machine (SVM) dan t-Distributed Stochastic Neighbor Embedding (t-SNE) untuk Klasifikasi Depresi." JURNAL MASYARAKAT INFORMATIKA 14, no. 2 (2023): 146–58. http://dx.doi.org/10.14710/jmasif.14.2.59513.

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Depresi merupakan salah satu gangguan kesehatan mental. Sekitar 300 juta jiwa atau 3,76% populasi di dunia dari segala usia dan komunitas menderita depresi. WHO memprediksi bahwa depresi akan menjadi penyebab kematian paling berdampak dalam 15 tahun ke depan. Penelitian terdahulu yang melakukan klasifikasi terhadap depresi untuk instrumen Depression Anxiety Stress Scales (DASS-42) masih sangat sedikit. Penelitian ini mengidentifikasi seseorang memiliki kemungkinan depresi, melalui proses pelatihan model klasifikasi menggunakan metode Support Vector Machine dan t-Distributed Stochastic Neighbor Embedding pada set data DASS-42. Set data DASS-42 terdiri dari 39.776 data dan dapat digunakan untuk mengklasifikasi 3 fenomena yang berbeda yaitu, depresi, stress dan kecemasan. Model Support Vector Machine dilatih menggunakan data DASS-42 yang telah dibersihkan, normalisasi dan balancing serta menggunakan atribut yang telah direduksi melalui proses reduksi dimensi t-Distributed Stochastic Neighbor Embedding. Data latih dan data uji dibagi dengan rasio 80:20. Berdasarkan hasil pengujian, implementasi metode Support Vector Machine (SVM) dan t-Distributed Stochastic Neighbor Embedding (t-SNE) untuk klasifikasi depresi pada data DASS-42 menunjukkan performa yang lebih baik dengan akurasi terbaik sebesar 100% pada data sebelum balancing dan 91,71% pada data setelah balancing.

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Valente, Daria, Chiara De Gregorio, Valeria Torti, et al. "Finding Meanings in Low Dimensional Structures: Stochastic Neighbor Embedding Applied to the Analysis of Indri indri Vocal Repertoire." Animals 9, no. 5 (2019): 243. http://dx.doi.org/10.3390/ani9050243.

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Although there is a growing number of researches focusing on acoustic communication, the lack of shared analytic approaches leads to inconsistency among studies. Here, we introduced a computational method used to examine 3360 calls recorded from wild indris (Indri indri) from 2005–2018. We split each sound into ten portions of equal length and, from each portion we extracted spectral coefficients, considering frequency values up to 15,000 Hz. We submitted the set of acoustic features first to a t-distributed stochastic neighbor embedding algorithm, then to a hard-clustering procedure using a k-means algorithm. The t-distributed stochastic neighbor embedding (t-SNE) mapping indicated the presence of eight different groups, consistent with the acoustic structure of the a priori identification of calls, while the cluster analysis revealed that an overlay between distinct call types might exist. Our results indicated that the t-distributed stochastic neighbor embedding (t-SNE), successfully been employed in several studies, showed a good performance also in the analysis of indris’ repertoire and may open new perspectives towards the achievement of shared methodical techniques for the comparison of animal vocal repertoires.

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Zhang, Haili, Pu Wang, Xuejin Gao, Yongsheng Qi, and Huihui Gao. "Process Data Visualization Using Bikernel t-Distributed Stochastic Neighbor Embedding." Industrial & Engineering Chemistry Research 59, no. 44 (2020): 19623–32. http://dx.doi.org/10.1021/acs.iecr.0c03333.

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Wang, Jing, Xiaobin Cheng, Xun Wang, et al. "On the solidification of the manifold of the t-distributed stochastic neighbour embedding for condition classification of machine tools." Engineering Research Express 3, no. 4 (2021): 045031. http://dx.doi.org/10.1088/2631-8695/ac37f0.

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Abstract t-distributed stochastic neighbour embedding (t-SNE) is of considerable interest in machining condition monitoring for feature selection. In this paper, the neural networks are introduced to solidify the manifold of the t-SNE prior to classification. This leads to the improved feature selection method, namely the Net-SNE. Conventional statistical features are first extracted from vibration signals to form a high dimensional feature vector. The redundancies in the feature vector are subsequently removed by the t-SNE. Then the neural networks build a mapping model between the high dimensional feature vector and the selected features. The new data is calculated directly using the mapping model. The experiments were conducted on a lathe and a milling machine to collect vibration signals under common working conditions. The K-nearest neighbour classifier is applied to a small sample case and a class-imbalance case to compare the classification performance with and without the Net-SNE. The results demonstrate that the Net-SNE has the advantage over the t-SNE, since it can mine the discriminative features and solidifiy the manifold in the calculation of the new data. Moreover, the proposed method significantly improves the classification accuracy by Net-SNE, along with better classification performance in data-limited situations.

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Kim, Kipoong, and Choongrak Kim. "A review on the t-distributed stochastic neighbors embedding." Korean Journal of Applied Statistics 36, no. 2 (2023): 167–73. http://dx.doi.org/10.5351/kjas.2023.36.2.167.

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Abbas, Ahmed Khudhair, Adil Ibrahim Khalil, and Sameera A. Abdulkader. "Social Touch Recognition Based on Support Vector Machine and T-Distributed Stochastic Neighbour Embedding as Pre-processing." IOP Conference Series: Materials Science and Engineering 1076, no. 1 (2021): 012042. http://dx.doi.org/10.1088/1757-899x/1076/1/012042.

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Urrutia, Robin, Diego Espejo, Natalia Evens, et al. "Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions." Sensors 23, no. 23 (2023): 9297. http://dx.doi.org/10.3390/s23239297.

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This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.

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Shi, Sha, Yefei Xu, Xiaoyang Xu, Xiaofan Mo, and Jun Ding. "A Preprocessing Manifold Learning Strategy Based on t-Distributed Stochastic Neighbor Embedding." Entropy 25, no. 7 (2023): 1065. http://dx.doi.org/10.3390/e25071065.

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In machine learning and data analysis, dimensionality reduction and high-dimensional data visualization can be accomplished by manifold learning using a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. We significantly improve this manifold learning scheme by introducing a preprocessing strategy for the t-SNE algorithm. In our preprocessing, we exploit Laplacian eigenmaps to reduce the high-dimensional data first, which can aggregate each data cluster and reduce the Kullback–Leibler divergence (KLD) remarkably. Moreover, the k-nearest-neighbor (KNN) algorithm is also involved in our preprocessing to enhance the visualization performance and reduce the computation and space complexity. We compare the performance of our strategy with that of the standard t-SNE on the MNIST dataset. The experiment results show that our strategy exhibits a stronger ability to separate different clusters as well as keep data of the same kind much closer to each other. Moreover, the KLD can be reduced by about 30% at the cost of increasing the complexity in terms of runtime by only 1–2%.

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Ma, Xiaobo, Yuchen Zhang, Fengshan Zhang, and Hongbin Liu. "Monitoring of papermaking wastewater treatment processes using t-distributed stochastic neighbor embedding." Journal of Environmental Chemical Engineering 9, no. 6 (2021): 106559. http://dx.doi.org/10.1016/j.jece.2021.106559.

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Cieslak, Matthew C., Ann M. Castelfranco, Vittoria Roncalli, Petra H. Lenz, and Daniel K. Hartline. "t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis." Marine Genomics 51 (June 2020): 100723. http://dx.doi.org/10.1016/j.margen.2019.100723.

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Zhu, Ye, and Kai Ming Ting. "Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel." Journal of Artificial Intelligence Research 71 (August 2, 2021): 667–95. http://dx.doi.org/10.1613/jair.1.12904.

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This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. Isolation kernel outperforms Gaussian kernel in two aspects. First, the use of Isolation kernel in t-SNE overcomes the drawback of misrepresenting some structures in the data, which often occurs when Gaussian kernel is applied in t-SNE. This is because Gaussian kernel determines each local bandwidth based on one local point only, while Isolation kernel is derived directly from the data based on space partitioning. Second, the use of Isolation kernel yields a more efficient similarity computation because data-dependent Isolation kernel has only one parameter that needs to be tuned. In contrast, the use of data-independent Gaussian kernel increases the computational cost by determining n bandwidths for a dataset of n points. As the root cause of these deficiencies in t-SNE is Gaussian kernel, we show that simply replacing Gaussian kernel with Isolation kernel in t-SNE significantly improves the quality of the final visualisation output (without creating misrepresented structures) and removes one key obstacle that prevents t-SNE from processing large datasets. Moreover, Isolation kernel enables t-SNE to deal with large-scale datasets in less runtime without trading off accuracy, unlike existing methods in speeding up t-SNE.

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Vyšata, Oldřich, Ondřej Ťupa, Aleš Procházka, et al. "Classification of Ataxic Gait." Sensors 21, no. 16 (2021): 5576. http://dx.doi.org/10.3390/s21165576.

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Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding.

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Hany, Maha, Shaheera Rashwan, and Neveen M. Abdelmotilib. "A Machine Learning Method for Prediction of Yogurt Quality and Consumers Preferencesusing Sensory Attributes and Image Processing Techniques." Machine Learning and Applications: An International Journal 10, no. 1 (2023): 1–7. http://dx.doi.org/10.5121/mlaij.2023.10101.

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Prediction of quality and consumers’ preferences is essential task for food producers to improve their market share and reduce any gap in food safety standards. In this paper, we develop a machine learning method to predict yogurt preferences based on the sensory attributes and analysis of samples’ images using image processing texture and color feature extraction techniques. We compare three unsupervised ML feature selection techniques (Principal Component Analysis and Independent Component Analysis and t-distributed Stochastic Neighbour Embedding) with one supervised ML feature selection technique (Linear Discriminant Analysis) in terms of accuracy of classification. Results show the efficiency of the supervised ML feature selection technique over the traditional feature selection techniques.

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Barnard, A. S., and G. Opletal. "Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning." Nanoscale 11, no. 48 (2019): 23165–72. http://dx.doi.org/10.1039/c9nr03940f.

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Verma, Meetu, Gal Matijevič, Carsten Denker та ін. "Classification of High-resolution Solar Hα Spectra Using t-distributed Stochastic Neighbor Embedding". Astrophysical Journal 907, № 1 (2021): 54. http://dx.doi.org/10.3847/1538-4357/abcd95.

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Wang, Zhi‐Lei, Toshio Ogawa, and Yoshitaka Adachi. "Persistent‐Homology‐Based Microstructural Optimization of Materials Using t‐Distributed Stochastic Neighbor Embedding." Advanced Theory and Simulations 3, no. 7 (2020): 2000040. http://dx.doi.org/10.1002/adts.202000040.

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Kanaan-Izquierdo, Samir, Andrey Ziyatdinov, Maria Araceli Burgueño, and Alexandre Perera-Lluna. "Multiview: a software package for multiview pattern recognition methods." Bioinformatics 35, no. 16 (2018): 2877–79. http://dx.doi.org/10.1093/bioinformatics/bty1039.

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Abstract Summary Multiview datasets are the norm in bioinformatics, often under the label multi-omics. Multiview data are gathered from several experiments, measurements or feature sets available for the same subjects. Recent studies in pattern recognition have shown the advantage of using multiview methods of clustering and dimensionality reduction; however, none of these methods are readily available to the extent of our knowledge. Multiview extensions of four well-known pattern recognition methods are proposed here. Three multiview dimensionality reduction methods: multiview t-distributed stochastic neighbour embedding, multiview multidimensional scaling and multiview minimum curvilinearity embedding, as well as a multiview spectral clustering method. Often they produce better results than their single-view counterparts, tested here on four multiview datasets. Availability and implementation R package at the B2SLab site: http://b2slab.upc.edu/software-and-tutorials/ and Python package: https://pypi.python.org/pypi/multiview. Supplementary information Supplementary data are available at Bioinformatics online.

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Lu, Weipeng, and Xuefeng Yan. "Industrial process data visualization based on a deep enhanced t-distributed stochastic neighbor embedding neural network." Assembly Automation 42, no. 2 (2022): 268–77. http://dx.doi.org/10.1108/aa-09-2021-0123.

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Purpose The purpose of this paper is to propose a approach for data visualization and industrial process monitoring. Design/methodology/approach A deep enhanced t-distributed stochastic neighbor embedding (DESNE) neural network is proposed for data visualization and process monitoring. The DESNE is composed of two deep neural networks: stacked variant auto-encoder (SVAE) and a deep label-guided t-stochastic neighbor embedding (DLSNE) neural network. In the DESNE network, SVAE extracts informative features of the raw data set, and then DLSNE projects the extracted features to a two dimensional graph. Findings The proposed DESNE is verified on the Tennessee Eastman process and a real data set of blade icing of wind turbines. The results indicate that DESNE outperforms some visualization methods in process monitoring. Originality/value This paper has significant originality. A stacked variant auto-encoder is proposed for feature extraction. The stacked variant auto-encoder can improve the separation among classes. A deep label-guided t-SNE is proposed for visualization. A novel visualization-based process monitoring method is proposed.

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Rodosthenous, Theodoulos, Vahid Shahrezaei, and Marina Evangelou. "Multi-view data visualisation via manifold learning." PeerJ Computer Science 10 (May 24, 2024): e1993. http://dx.doi.org/10.7717/peerj-cs.1993.

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Non-linear dimensionality reduction can be performed by manifold learning approaches, such as stochastic neighbour embedding (SNE), locally linear embedding (LLE) and isometric feature mapping (ISOMAP). These methods aim to produce two or three latent embeddings, primarily to visualise the data in intelligible representations. This manuscript proposes extensions of Student’s t-distributed SNE (t-SNE), LLE and ISOMAP, for dimensionality reduction and visualisation of multi-view data. Multi-view data refers to multiple types of data generated from the same samples. The proposed multi-view approaches provide more comprehensible projections of the samples compared to the ones obtained by visualising each data-view separately. Commonly, visualisation is used for identifying underlying patterns within the samples. By incorporating the obtained low-dimensional embeddings from the multi-view manifold approaches into the K-means clustering algorithm, it is shown that clusters of the samples are accurately identified. Through extensive comparisons of novel and existing multi-view manifold learning algorithms on real and synthetic data, the proposed multi-view extension of t-SNE, named multi-SNE, is found to have the best performance, quantified both qualitatively and quantitatively by assessing the clusterings obtained. The applicability of multi-SNE is illustrated by its implementation in the newly developed and challenging multi-omics single-cell data. The aim is to visualise and identify cell heterogeneity and cell types in biological tissues relevant to health and disease. In this application, multi-SNE provides an improved performance over single-view manifold learning approaches and a promising solution for unified clustering of multi-omics single-cell data.

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Koolstra, Kirsten, Peter Börnert, Boudewijn P. F. Lelieveldt, Andrew Webb, and Oleh Dzyubachyk. "Stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries." Magnetic Resonance Materials in Physics, Biology and Medicine 35, no. 2 (2021): 223–34. http://dx.doi.org/10.1007/s10334-021-00963-8.

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Abstract Objective To visualize the encoding capability of magnetic resonance fingerprinting (MRF) dictionaries. Materials and methods High-dimensional MRF dictionaries were simulated and embedded into a lower-dimensional space using t-distributed stochastic neighbor embedding (t-SNE). The embeddings were visualized via colors as a surrogate for location in low-dimensional space. First, we illustrate this technique on three different MRF sequences. We then compare the resulting embeddings and the color-coded dictionary maps to these obtained with a singular value decomposition (SVD) dimensionality reduction technique. We validate the t-SNE approach with measures based on existing quantitative measures of encoding capability using the Euclidean distance. Finally, we use t-SNE to visualize MRF sequences resulting from an MRF sequence optimization algorithm. Results t-SNE was able to show clear differences between the color-coded dictionary maps of three MRF sequences. SVD showed smaller differences between different sequences. These findings were confirmed by quantitative measures of encoding. t-SNE was also able to visualize differences in encoding capability between subsequent iterations of an MRF sequence optimization algorithm. Discussion This visualization approach enables comparison of the encoding capability of different MRF sequences. This technique can be used as a confirmation tool in MRF sequence optimization.

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Ali, Sarwan, and Murray Patterson. "Improving ISOMAP Efficiency with RKS: A Comparative Study with t-Distributed Stochastic Neighbor Embedding on Protein Sequences." J 6, no. 4 (2023): 579–91. http://dx.doi.org/10.3390/j6040038.

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Data visualization plays a crucial role in gaining insights from high-dimensional datasets. ISOMAP is a popular algorithm that maps high-dimensional data into a lower-dimensional space while preserving the underlying geometric structure. However, ISOMAP can be computationally expensive, especially for large datasets, due to the computation of the pairwise distances between data points. The motivation behind this study is to improve efficiency by leveraging an approximate method, which is based on random kitchen sinks (RKS). This approach provides a faster way to compute the kernel matrix. Using RKS significantly reduces the computational complexity of ISOMAP while still obtaining a meaningful low-dimensional representation of the data. We compare the performance of the approximate ISOMAP approach using RKS with the traditional t-SNE algorithm. The comparison involves computing the distance matrix using the original high-dimensional data and the low-dimensional data computed from both t-SNE and ISOMAP. The quality of the low-dimensional embeddings is measured using several metrics, including mean squared error (MSE), mean absolute error (MAE), and explained variance score (EVS). Additionally, the runtime of each algorithm is recorded to assess its computational efficiency. The comparison is conducted on a set of protein sequences, used in many bioinformatics tasks. We use three different embedding methods based on k-mers, minimizers, and position weight matrix (PWM) to capture various aspects of the underlying structure and the relationships between the protein sequences. By comparing different embeddings and by evaluating the effectiveness of the approximate ISOMAP approach using RKS and comparing it against t-SNE, we provide insights on the efficacy of our proposed approach. Our goal is to retain the quality of the low-dimensional embeddings while improving the computational performance.

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Leon-Medina, Jersson X., Maribel Anaya, Francesc Pozo, and Diego Tibaduiza. "Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task." Sensors 20, no. 17 (2020): 4834. http://dx.doi.org/10.3390/s20174834.

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A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

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Mr., K. Anguraju. "AN INTELLIGENT SELF-ORGANIZING LSTM MODEL FOR COMORBIDITY ASSESSMENT OF PERIPHERAL ARTERY DISEASE METHODS." International Journal of Advances in Engineering & Scientific Research 12, no. 1 (2025): 138–50. https://doi.org/10.5281/zenodo.14928824.

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Peripheral Artery Disease (PAD) often coexists with other cardiovascular conditions, necessitating accurate comorbidity assessment. This study presents an intelligent self-organizing Long Short-Term Memory (LSTM) model to classify and assess PAD comorbidities using radial artery pulse wave data. A case-control study was conducted with 202 healthy individuals, 187 patients with CoronaryArteryDisease (CAD), and 73 patients with Heart Failure (HF), sourced from Shanghai Municipal Hospitals of Traditional Chinese Medicine. Pulse wave data were recorded, denoised, and standardized using min-max normalization.Four deep learning models—Bidirectional LSTM (Bi-LSTM), Convolutional Neural Network (CNN), Gated Recurrent Units (GRU), and LSTM—were employed to classify pulse wave characteristics. Data imbalance was addressed using Synthetic Minority Oversampling Technique (SMOTE), improving minority class representation. Model performance was evaluated using accuracy, precision, recall, F1-score, specificity, and AUC metrics.The results demonstrated that SMOTE-enhanced data distribution improved classification performance. Additionally, t-distributed Stochastic Neighbor Embedding (t-SNE) was applied to visualize high-dimensional data relationships. The proposed LSTM-based model exhibited superior long-term dependency learning, offering a robust approach for PAD comorbidity assessment. Keywords: Deep Learning, Pulse Wave Analysis, Synthetic Minority Oversampling Technique (SMOTE), t-Distributed Stochastic Neighbor Embedding (t-SNE) and Comorbidity Assessment.

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Gao, Lianru, Daixin Gu, Lina Zhuang, Jinchang Ren, Dong Yang, and Bing Zhang. "Combining t-Distributed Stochastic Neighbor Embedding With Convolutional Neural Networks for Hyperspectral Image Classification." IEEE Geoscience and Remote Sensing Letters 17, no. 8 (2020): 1368–72. http://dx.doi.org/10.1109/lgrs.2019.2945122.

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Zhou, Hongyu, Feng Wang, and Peng Tao. "t-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations." Journal of Chemical Theory and Computation 14, no. 11 (2018): 5499–510. http://dx.doi.org/10.1021/acs.jctc.8b00652.

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Zhu, Wenbo, Zachary T. Webb, Kaitian Mao, and José Romagnoli. "A Deep Learning Approach for Process Data Visualization Using t-Distributed Stochastic Neighbor Embedding." Industrial & Engineering Chemistry Research 58, no. 22 (2019): 9564–75. http://dx.doi.org/10.1021/acs.iecr.9b00975.

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Cheng, Minjie, Dixin Luo, and Hongteng Xu. "WatE: A Wasserstein t-distributed Embedding Method for Information-enriched Graph Visualization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 15 (2025): 16010–18. https://doi.org/10.1609/aaai.v39i15.33758.

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As a fundamental problem of graph analysis, graph visualization aims to embed a set of graphs in a low-dimensional (e.g., 2D) space and provide insights into their distribution and clustering structure. Focusing on this problem, we propose a novel Wasserstein t-distributed embedding (WatE) method, leading to an information-enriched graph visualization paradigm. Our method learns a graph neural network to represent each graph as the mean and covariance of its node embedding distribution. Accordingly, our method can visualize each graph as an ellipse (determined by the mean and the covariance) rather than a single point. The positions of different ellipses reveal the relations among different graphs as traditional visualization methods do, while the size and shape of an ellipse preserve the node-level structural information of the corresponding graph. We propose a regularized t-distributed stochastic neighbor embedding (Rt-SNE) framework to learn the visualization model, deriving a Wasserstein distance-based Student's t-distribution of graph pairs and fitting the distribution to the data distribution under regularization. Both subjective and objective evaluations demonstrate that WatE achieves encouraging performance in various graph visualization and clustering tasks.

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Gajjar, Pranshav, Naishadh Mehta, and Pooja Shah. "Quadruplet loss and SqueezeNets for Covid-19 detection from Chest-X ray." Computer Science Journal of Moldova 30, no. 2 (89) (2022): 214–22. http://dx.doi.org/10.56415/csjm.v30.12.

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The Coronavirus Pandemic triggered by SARS-CoV-2 has wreaked havoc on the planet and is expanding exponentially. While scanning methods, including CT scans and chest X-rays, are commonly used, artificial intelligence implementations are also deployed for COVID-based pneumonia detection. Due to image biases in X-ray data, bilateral filtration and Histogram Equalization are used followed by lung segmentation by a U-Net, which successfully segmented 83.2\% of the collected dataset. The segmented lungs are fed into a Quadruplet Network with SqueezeNet encoders for increased computational efficiency and high-level embeddings generation. The embeddings are computed using a Multi-Layer Perceptron and visualized by T-SNE (T-Distributed Stochastic Neighbor Embedding) scatterplots. The proposed research results in a 94.6\% classifying accuracy which is 2\% more than the baseline Convolutional Neural Network and a 90.2\% decrease in prediction time.

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Fang, Xian, Zhixin Tie, Yinan Guan, and Shanshan Rao. "Quasi-cluster centers clustering algorithm based on potential entropy and t-distributed stochastic neighbor embedding." Soft Computing 23, no. 14 (2018): 5645–57. http://dx.doi.org/10.1007/s00500-018-3221-y.

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Tu, Deyu, Jinde Zheng, Zhanwei Jiang, and Haiyang Pan. "Multiscale Distribution Entropy and t-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings." Entropy 20, no. 5 (2018): 360. http://dx.doi.org/10.3390/e20050360.

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Tadjer, Amine, Reider B. Bratvold, and Remus G. Hanea. "Efficient Dimensionality Reduction Methods in Reservoir History Matching." Energies 14, no. 11 (2021): 3137. http://dx.doi.org/10.3390/en14113137.

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Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.

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Ali, Mohsin, Jitendra Choudhary, and Tanmay Kasbe. "A hybrid model for data visualization using linear algebra methods and machine learning algorithm." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 463. http://dx.doi.org/10.11591/ijeecs.v33.i1.pp463-475.

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The t-distributed stochastic neighbor embedding (t-SNE) is a powerful technique for visualizing high-dimensional datasets. By reducing the dimensionality of the data, t-SNE transforms it into a format that can be more easily understood and analyzed. The existing approach is to visualize high-dimensional data but not deeply visualize. This paper proposes a model that enhances visualization and improves the accuracy. The proposed model combines the non-linear embedding technique t-SNE, the linear dimensionality reduction method principal component analysis (PCA), and the QR decomposition algorithm for discovering eigenvalues and eigenvectors. In Addition, we quantitatively compare the proposed model QRPCA-t-SNE with PCA-t-SNE using the following criteria: data visualization with different perplexity and different principal components, confusion matrix, model score, mean square error (MSE), training, testing accuracy, receiver operating characteristic curve (ROC) score, and AUC score.

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Ali, Mohsin, Jitendra Choudhary, and Tanmay Kasbe. "A hybrid model for data visualization using linear algebramethods and machine learning algorithm." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 463–75. https://doi.org/10.11591/ijeecs.v33.i1.pp463-475.

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The t-distributed stochastic neighbor embedding (t-SNE) is a powerful technique for visualizing high-dimensional datasets. By reducing the dimensionality of the data, t-SNE transforms it into a format that can be more easily understood and analyzed. The existing approach is to visualize high-dimensional data but not deeply visualize. This paper proposes a model that enhances visualization and improves the accuracy. The proposed model combines the non-linear embedding technique t-SNE, the linear dimensionality reduction method principal component analysis (PCA), and the QR decomposition algorithm for discovering eigenvalues and eigenvectors. In Addition, we quantitatively compare the proposed model QRPCA-t-SNE with PCA-t-SNE using the following criteria: data visualization with different perplexity and different principal components, confusion matrix, model score, mean square error (MSE), training, testing accuracy, receiver operating characteristic curve (ROC) score, and AUC score.

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Wang, Xiang, and Han Jiang. "Gearbox Fault Diagnosis Based on Refined Time-Shift Multiscale Reverse Dispersion Entropy and Optimised Support Vector Machine." Machines 11, no. 6 (2023): 646. http://dx.doi.org/10.3390/machines11060646.

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The fault diagnosis of a gearbox is crucial to ensure its safe operation. Entropy has become a common tool for measuring the complexity of time series. However, entropy bias may occur when the data are not long enough or the scale becomes larger. This paper proposes a gearbox fault diagnosis method based on Refined Time-Shifted Multiscale Reverse Dispersion Entropy (RTSMRDE), t-distributed Stochastic Neighbour Embedding (t-SNE), and the Sparrow Search Algorithm Support Vector Machine (SSA-SVM). First, the proposed RTSMRDE was used to calculate the multiscale fault features. By incorporating the refined time-shift method into Multiscale Reverse Dispersion Entropy (MRDE), errors that arose during the processing of complex time series could be effectively reduced. Second, the t-SNE algorithm was utilized to extract sensitive features from the multiscale, high-dimensional fault features. Finally, the low-dimensional feature matrix was input into SSA-SVM for fault diagnosis. Two gearbox experiments showed that the diagnostic model proposed in this paper had an accuracy rate of 100%, and the proposed model performed better than other methods in terms of diagnostic performance.

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Nigro, Hector. "Reducción de dimensionalidad en grandes volúmenes de datos usando PCA y t-SNE." Revista Ingeniería, Matemáticas y Ciencias de la Información 12, no. 23 (2025): 139–46. https://doi.org/10.21017/rimci.1133.

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Este artículo explora el uso de técnicas de reducción de dimensionalidad, específicamente Análisis de Componentes Principales (PCA) y t-Distributed Stochastic Neighbor Embedding (t-SNE), aplicadas al tratamiento de grandes volúmenes de datos. Se analiza su eficacia en la visualización, preprocesamiento y mejora del rendimiento de modelos de aprendizaje automático. A través de experimentos con datasets públicos, se evalúan los resultados en términos de retención de información, tiempo de cómputo y calidad de representación. Los hallazgos destacan los contextos ideales para cada técnica y ofrecen lineamientos para su aplicación práctica.

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Sai, Kalyana Pranitha Buddiga. "Navigating the Complexity of Big Data: Exploring Dimensionality Reduction Methods." Journal of Scientific and Engineering Research 7, no. 8 (2020): 220–23. https://doi.org/10.5281/zenodo.11216323.

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In the era of big data, the exponential growth of data volume and dimensionality poses significant challenges for data analysis and interpretation. Dimensionality reduction techniques play a crucial role in managing the complexity of high-dimensional datasets by extracting essential features while preserving the inherent structure and information. This paper provides a comprehensive overview of dimensionality reduction methods, ranging from classical techniques like Principal Component Analysis (PCA) to advance nonlinear methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) and autoencoders. Through this study, we explore the strengths, limitations, and applicability of different dimensionality reduction approaches across various domains. Additionally, the paper focusses on practices, and emerging trends in dimensionality reduction research, aiming to guide researchers and practitioners in navigating the complexities of big data analytics.

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Acuff, Nicole V., and Joel Linden. "Using Visualization of t-Distributed Stochastic Neighbor Embedding To Identify Immune Cell Subsets in Mouse Tumors." Journal of Immunology 198, no. 11 (2017): 4539–46. http://dx.doi.org/10.4049/jimmunol.1602077.

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DUTAĞACI, HELİN. "Using t-distributed stochastic neighbor embedding for visualization and segmentation of 3D point clouds of plants." Turkish Journal of Electrical Engineering and Computer Sciences 31, no. 5 (2023): 792–813. http://dx.doi.org/10.55730/1300-0632.4018.

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Kuhaneswaran, Banujan, Abishethvarman Vadivel, Ashansa Wijeratne, et al. "Exploring the Educational Landscape of ChatGPT: A Topic Modeling Approach on Twitter Data." Sri Lanka Journal of Social Sciences and Humanities 4, no. 1 (2024): 1–12. http://dx.doi.org/10.4038/sljssh.v4i1.114.

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In the rapidly evolving landscape of Artificial Intelligence (AI), platforms like ChatGPT are reshaping the educational domain, prompting deeper explorations into the nature and depth of this intersection. This study aimed to systematically uncover the prevailing sentiments, concerns and discussions on Twitter surrounding ChatGPT’s role in education. Through an extensive data collection process, over 3.8 million tweets were initially gathered, followed by rigorous refining processes that included expert-driven tweet labelling and subsequent classification using Machine Learning (ML) and deep learning models. The cleaned dataset underwent a series of preprocessing steps and feature extraction and was ultimately subjected to Latent Dirichlet Allocation (LDA) for topic modelling. Our findings unveiled 15 distinct topics that broadly spanned common discussions, AI implementation, and its potential impacts. The data’s visualisation using t-distributed stochastic neighbour embedding (t-SNE) showcased a dense central clustering of these topics. In conclusion, our research underscores the multi-faceted dialogues on AI, particularly ChatGPT, in education, emphasising the pressing need for continued discourse and research as AI tools further integrate into our educational paradigms.

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Bruni Prenestino, Francesco, Enrico Barbierato, and Alice Gatti. "Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures." Future Internet 17, no. 2 (2025): 95. https://doi.org/10.3390/fi17020095.

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Generating high-quality synthetic data is essential for advancing machine learning applications in financial time series, where data scarcity and privacy concerns often pose significant challenges. This study proposes a novel hybrid architecture that combines variational autoencoders (VAEs) with Markov Chain Monte Carlo (MCMC) sampling to enhance the generation of robust synthetic sequential data. The model leverages Gated Recurrent Unit (GRU) layers for capturing long-term temporal dependencies and MCMC sampling for effective latent space exploration, ensuring high variability and accuracy. Experimental evaluations on datasets of Google, Tesla, and Nestlé stock prices demonstrate the model’s superior performance in preserving statistical and temporal patterns, as validated by quantitative metrics (discriminative and predictive scores), statistical tests (Kolmogorov–Smirnov), and t-Distributed Stochastic Neighbour Embedding (t-SNE) visualisations. The experiments reveal the model’s scalability, maintaining high fidelity even under augmented dataset sizes and missing data scenarios. These findings position the proposed framework as a computationally efficient and structurally simple alternative to Generative Adversarial Network (GAN)-based methods, suitable for real-world applications in data-driven financial modelling.

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Liu, Honghua, Jing Yang, Ming Ye, et al. "Using t-distributed Stochastic Neighbor Embedding (t-SNE) for cluster analysis and spatial zone delineation of groundwater geochemistry data." Journal of Hydrology 597 (June 2021): 126146. http://dx.doi.org/10.1016/j.jhydrol.2021.126146.

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Michalak, Krzysztof. "Visualizing Combinatorial Search Spaces with Low-Dimensional Euclidean Embedding." ACM SIGEVOlution 15, no. 4 (2022): 1–8. http://dx.doi.org/10.1145/3584367.3584369.

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The low dimensional Euclidean embedding method (LDEE) allows visualizing combinatorial search spaces by mapping to the Euclidean space R k (with k = 2 or 3 in practice). The mapping of a combinatorial search space Ω to R k is obtained by first running the t-SNE (t-distributed stochastic neighbor embedding) algorithm with an appropriate probability distribution used for the space Ω (for example the Mallows distribution for permutation spaces). Subsequently, the vacuum embedding algorithm, proposed in this article, is used to ensure good visual separation of solutions in R k . The LDEE method maps solutions to a regular grid in R k , which can be used for plotting various kinds of information. Apart from solution evaluations or comparisons of multiple objectives, the proposed method can be used for analyzing the behavior of the population in population-based metaheuristics, the working of genetic operators, etc. This newsletter contribution summarizes a recent research article [1].

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Patel, Tulsi, Mark W. Jones, and Thomas Redfern. "Manifold Explorer: Satellite Image Labelling and Clustering Tool with Using Deep Convolutional Autoencoders." Algorithms 16, no. 10 (2023): 469. http://dx.doi.org/10.3390/a16100469.

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We present a novel approach to providing greater insight into the characteristics of an unlabelled dataset, increasing the efficiency with which labelled datasets can be created. We leverage dimension-reduction techniques in combination with autoencoders to create an efficient feature representation for image tiles derived from remote sensing satellite imagery. The proposed methodology consists of two main stages. Firstly, an autoencoder network is utilised to reduce the high-dimensional image tile data into a compact and expressive latentfeature representation. Subsequently, features are further reduced to a two-dimensional embedding space using the manifold learning algorithm Uniform Manifold Approximation and Projection (UMAP) and t-distributed Stochastic Neighbour Embedding (t-SNE). This step enables the visualization of the image tile clusters in a 2D plot, providing an intuitive and interactive representation that can be used to aid rapid and geographically distributed image labelling. To facilitate the labelling process, our approach allows users to interact with the 2D visualization and label clusters based on their domain knowledge. In cases where certain classes are not effectively separated, users can re-apply dimension reduction to interactively refine subsets of clusters and achieve better class separation, enabling a comprehensively labelled dataset. We evaluate the proposed approach on real-world remote sensing satellite image datasets and demonstrate its effectiveness in achieving accurate and efficient image tile clustering and labelling. Users actively participate in the labelling process through our interactive approach, leading to enhanced relevance of the labelled data, by allowing domain experts to contribute their expertise and enrich the dataset for improved downstream analysis and applications.

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Tunvirachaisakul, Chavit, Thitiporn Supasitthumrong, Sookjareon Tangwongchai, et al. "Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer’s Disease Tests: A Multivariate and Machine Learning Study." Dementia and Geriatric Cognitive Disorders 45, no. 1-2 (2018): 38–48. http://dx.doi.org/10.1159/000487232.

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Background: The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer’s dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. Objectives: To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. Methods: The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). Results: MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. Conclusions: The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI.

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Levitas, Joseph, Konstantin Yavilberg, Oleg Korol, and Genadi Man. "Prediction of Auto Insurance Risk Based on t-SNE Dimensionality Reduction." Advances in Artificial Intelligence and Machine Learning 02, no. 04 (2022): 567–79. http://dx.doi.org/10.54364/aaiml.2022.1139.

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Correct risk estimation of policyholders is of great significance to auto insurance companies. While the current tools used in this field have been proven in practice to be quite efficient and beneficial, we argue that there is still a lot of room for development and improvement in the auto insurance risk estimation process. To this end, we develop a framework based on a combination of a neural network together with a dimensionality reduction technique t-SNE (t-distributed stochastic neighbour embedding). This enables us to visually represent the complex structure of the risk as a two-dimensional surface, while still preserving the properties of the local region in the features space. The obtained results, which are based on real insurance data, reveal a clear contrast between the high and the low risk policy holders, and indeed improve upon the actual risk estimation performed by the insurer. Due to the visual accessibility of the portfolio in this approach, we argue that this framework could be advantageous to the auto insurer, both as a main risk prediction tool and as an additional validation stage in other approaches.

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Tao, Shiyong, Weirong Chen, Shuna Jiang, Xinyu Liu, and Jiaxi Yu. "INTELLIGENT HEALTH STATUS DETECTION METHOD FOR LOCOMOTIVE FUEL CELL BASED ON DATA-DRIVEN TECHNIQUES." DYNA 96, no. 6 (2021): 633–39. http://dx.doi.org/10.6036/10290.

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Main drawbacks of fuel cell systems, namely, high cost, poor reliability, and short lifespan, limit the large-scale commercial application of fuel cell systems. The health status detection of fuel cell systems for locomotives is of great significance to the safe and stable operation of locomotives. To identify the failure modes of the fuel cell system accurately and quickly, this study proposed an intelligent health status detection method for locomotive fuel cells based on data-driven techniques. In this study, the actual test data of a 150-kW fuel cell system for locomotives was analyzed. The t-distributed stochastic neighbor embedding (t-SNE) algorithm was combined with the general regression neural network (GRNN) to intelligently detect the health status of the fuel cell system for locomotives. Specifically, t-SNE was used to process the high-dimensionality and strong coupling raw data of health status, enabling the dimensional reduction of the raw data to reflect essential features. Then, GRNN was used to identify the feature data to achieve the fast and accurate detection of the health status of the fuel cell system. Results show that the proposed method can effectively detect four health conditions, namely, normal state, high inlet coolant temperature, low air pressure, and low spray pump pressure, with a diagnostic accuracy of 98.75%. This study is applicable to the analysis of the actual measurement data of high-power level fuel cell systems and provides a reference for the health status detection of fuel cell systems for locomotives. Keywords: fuel cell system for locomotive; data-driven; general regression neural network; t-distributed stochastic neighbor embedding; health status detection

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Wu, Hao, Dahai Dai, and Xuesong Wang. "A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering." Sensors 19, no. 23 (2019): 5112. http://dx.doi.org/10.3390/s19235112.

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High-resolution range profile (HRRP) has attracted intensive attention from radar community because it is easy to acquire and analyze. However, most of the conventional algorithms require the prior information of targets, and they cannot process a large number of samples in real time. In this paper, a novel HRRP recognition method is proposed to classify unlabeled samples automatically where the number of categories is unknown. Firstly, with the preprocessing of HRRPs, we adopt principal component analysis (PCA) for dimensionality reduction of data. Afterwards, t-distributed stochastic neighbor embedding (t-SNE) with Barnes–Hut approximation is conducted for the visualization of high-dimensional data. It proves to reduce the dimensionality, which has significantly improved the computation speed. Finally, it is exhibited that the recognition performance with density-based clustering is superior to conventional algorithms under the condition of large azimuth angle ranges and low signal-to-noise ratio (SNR).

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Journal articles on the topic 'T-distributed Stochastic Neighbour Embedding'

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