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Journal articles on the topic 'T-stochastic neighbor embedding'

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Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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1

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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2

Huang, Yanyong, Kejun Guo, Xiuwen Yi, Jing Yu, Zongxin Shen, and Tianrui Li. "T-copula and Wasserstein distance-based stochastic neighbor embedding." Knowledge-Based Systems 243 (May 2022): 108431. http://dx.doi.org/10.1016/j.knosys.2022.108431.

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Valente, Daria, Chiara De Gregorio, Valeria Torti, Longondraza Miaretsoa, Olivier Friard, Rose Marie Randrianarison, Cristina Giacoma, and Marco Gamba. "Finding Meanings in Low Dimensional Structures: Stochastic Neighbor Embedding Applied to the Analysis of Indri indri Vocal Repertoire." Animals 9, no. 5 (May 15, 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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Yu, Meiting, Lingjun Zhao, Siqian Zhang, Boli Xiong, and Gangyao Kuang. "SAR target recognition using parametric supervised t-stochastic neighbor embedding." Remote Sensing Letters 8, no. 9 (May 28, 2017): 849–58. http://dx.doi.org/10.1080/2150704x.2017.1332795.

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5

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 (October 21, 2020): 19623–32. http://dx.doi.org/10.1021/acs.iecr.0c03333.

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6

Zhang, Qiang, Yi Yao, Dongsheng Zhou, and Rui Liu. "Motion Key-Frame Extraction by Using Optimized t-Stochastic Neighbor Embedding." Symmetry 7, no. 2 (April 21, 2015): 395–411. http://dx.doi.org/10.3390/sym7020395.

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7

Pitsianis, Nikos, Dimitris Floros, Alexandros-Stavros Iliopoulos, and Xiaobai Sun. "SG-t-SNE-Π: Swift Neighbor Embedding of Sparse Stochastic Graphs." Journal of Open Source Software 4, no. 39 (July 31, 2019): 1577. http://dx.doi.org/10.21105/joss.01577.

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8

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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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 (December 2021): 106559. http://dx.doi.org/10.1016/j.jece.2021.106559.

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10

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 (October 23, 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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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 (March 18, 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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12

Verma, Meetu, Gal Matijevič, Carsten Denker, Andrea Diercke, Ekaterina Dineva, Horst Balthasar, Robert Kamlah, Ioannis Kontogiannis, Christoph Kuckein, and Partha S. Pal. "Classification of High-resolution Solar Hα Spectra Using t-distributed Stochastic Neighbor Embedding." Astrophysical Journal 907, no. 1 (January 28, 2021): 54. http://dx.doi.org/10.3847/1538-4357/abcd95.

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13

Hu, Ying, Xiaobing Li, Lijia Wang, Baosan Han, and Shengdong Nie. "T-distribution stochastic neighbor embedding for fine brain functional parcellation on rs-fMRI." Brain Research Bulletin 162 (September 2020): 199–207. http://dx.doi.org/10.1016/j.brainresbull.2020.06.007.

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14

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 (June 5, 2020): 2000040. http://dx.doi.org/10.1002/adts.202000040.

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15

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 (August 27, 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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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) (July 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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17

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 (August 2020): 1368–72. http://dx.doi.org/10.1109/lgrs.2019.2945122.

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18

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 (September 25, 2018): 5499–510. http://dx.doi.org/10.1021/acs.jctc.8b00652.

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19

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 (May 16, 2019): 9564–75. http://dx.doi.org/10.1021/acs.iecr.9b00975.

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20

Tadjer, Amine, Reider B. Bratvold, and Remus G. Hanea. "Efficient Dimensionality Reduction Methods in Reservoir History Matching." Energies 14, no. 11 (May 27, 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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21

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 (May 11, 2018): 5645–57. http://dx.doi.org/10.1007/s00500-018-3221-y.

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22

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 (May 11, 2018): 360. http://dx.doi.org/10.3390/e20050360.

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23

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 (May 3, 2017): 4539–46. http://dx.doi.org/10.4049/jimmunol.1602077.

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24

Demidova, Liliya A., and Artyom V. Gorchakov. "Fuzzy Information Discrimination Measures and Their Application to Low Dimensional Embedding Construction in the UMAP Algorithm." Journal of Imaging 8, no. 4 (April 15, 2022): 113. http://dx.doi.org/10.3390/jimaging8040113.

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Dimensionality reduction techniques are often used by researchers in order to make high dimensional data easier to interpret visually, as data visualization is only possible in low dimensional spaces. Recent research in nonlinear dimensionality reduction introduced many effective algorithms, including t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), dimensionality reduction technique based on triplet constraints (TriMAP), and pairwise controlled manifold approximation (PaCMAP), aimed to preserve both the local and global structure of high dimensional data while reducing the dimensionality. The UMAP algorithm has found its application in bioinformatics, genetics, genomics, and has been widely used to improve the accuracy of other machine learning algorithms. In this research, we compare the performance of different fuzzy information discrimination measures used as loss functions in the UMAP algorithm while constructing low dimensional embeddings. In order to achieve this, we derive the gradients of the considered losses analytically and employ the Adam algorithm during the loss function optimization process. From the conducted experimental studies we conclude that the use of either the logarithmic fuzzy cross entropy loss without reduced repulsion or the symmetric logarithmic fuzzy cross entropy loss with sufficiently large neighbor count leads to better global structure preservation of the original multidimensional data when compared to the loss function used in the original UMAP algorithm implementation.
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Liu, Honghua, Jing Yang, Ming Ye, Scott C. James, Zhonghua Tang, Jie Dong, and Tongju Xing. "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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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 (November 1, 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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Gu, Haoyu, and Li Wang. "Modified t-Distribution Stochastic Neighbor Embedding Using Augmented Kernel Mahalanobis-Distance for Dynamic Multimode Chemical Process Monitoring." International Journal of Chemical Engineering 2022 (December 29, 2022): 1–19. http://dx.doi.org/10.1155/2022/8460463.

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The traditional data-driven process monitoring methods may not be applicable for the system which has dynamic and multimode characteristics. In this paper, a novel scheme named modified t-distribution stochastic neighbor embedding using augmented Mahalanobis-distance for dynamic multimode chemical process monitoring (AKMD-t-SNE) is proposed to realize dynamic multimodal process monitoring. First, the augmented matrix strategy is utilized to ensure the sample contains the autocorrelation of the process. Moreover, AKMD-t-SNE method eliminates the scale and spatial distribution differences among multiple modes by calculating the kernel Mahalanobis distance between the samples to establish the global model. The features extracted via the proposed method are obviously non-Gaussian, and there will be a deviation in the construction of traditional statistics. Then, the support vector data description (SVDD) method is used to construct statistics to deal with this problem. In addition, a hybrid correlation coefficient method (HCC) is proposed to achieve fault isolation and improve the accuracy of isolation results. The advantages of the proposed scheme are illustrated by a numerical case and the Multimode Tennessee Eastman Process (MTEP) benchmark.
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28

Pouyet, Emeline, Neda Rohani, Aggelos K. Katsaggelos, Oliver Cossairt, and Marc Walton. "Innovative data reduction and visualization strategy for hyperspectral imaging datasets using t-SNE approach." Pure and Applied Chemistry 90, no. 3 (February 23, 2018): 493–506. http://dx.doi.org/10.1515/pac-2017-0907.

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AbstractVisible hyperspectral imaging (HSI) is a fast and non-invasive imaging method that has been adapted by the field of conservation science to study painted surfaces. By collecting reflectance spectra from a 2D surface, the resulting 3D hyperspectral data cube contains millions of recorded spectra. While processing such large amounts of spectra poses an analytical and computational challenge, it also opens new opportunities to apply powerful methods of multivariate analysis for data evaluation. With the intent of expanding current data treatment of hyperspectral datasets, an innovative approach for data reduction and visualization is presented in this article. It uses a statistical embedding method known as t-distributed stochastic neighbor embedding (t-SNE) to provide a non-linear representation of spectral features in a lower 2D space. The efficiency of the proposed method for painted surfaces from cultural heritage is established through the study of laboratory prepared paint mock-ups, and medieval French illuminated manuscript.
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Leon-Medina, Jersson X., Maribel Anaya, and Diego Alexander Tibaduiza. "T-Distributed Stochastic Neighbor Embedding to Improve the Discrimination of Yogurt Using a Multistep Amperometry Electronic Tongue." ECS Meeting Abstracts MA2021-01, no. 64 (May 30, 2021): 2061. http://dx.doi.org/10.1149/ma2021-01642061mtgabs.

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Zarzar, Mouayad, Eliza Razak, Zaw Zaw Htike, and Faridah Yusof. "Early Diagnosis of Non-Small-Cell Lung Carcinoma from Gene Expression Using t-Distributed Stochastic Neighbor Embedding." Advanced Science Letters 21, no. 11 (November 1, 2015): 3550–53. http://dx.doi.org/10.1166/asl.2015.6587.

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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 (November 22, 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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Li, Wentian, Jane E. Cerise, Yaning Yang, and Henry Han. "Application of t-SNE to human genetic data." Journal of Bioinformatics and Computational Biology 15, no. 04 (August 2017): 1750017. http://dx.doi.org/10.1142/s0219720017500172.

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The t-distributed stochastic neighbor embedding t-SNE is a new dimension reduction and visualization technique for high-dimensional data. t-SNE is rarely applied to human genetic data, even though it is commonly used in other data-intensive biological fields, such as single-cell genomics. We explore the applicability of t-SNE to human genetic data and make these observations: (i) similar to previously used dimension reduction techniques such as principal component analysis (PCA), t-SNE is able to separate samples from different continents; (ii) unlike PCA, t-SNE is more robust with respect to the presence of outliers; (iii) t-SNE is able to display both continental and sub-continental patterns in a single plot. We conclude that the ability for t-SNE to reveal population stratification at different scales could be useful for human genetic association studies.
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Abdelmoula, Walid M., Benjamin Balluff, Sonja Englert, Jouke Dijkstra, Marcel J. T. Reinders, Axel Walch, Liam A. McDonnell, and Boudewijn P. F. Lelieveldt. "Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data." Proceedings of the National Academy of Sciences 113, no. 43 (October 10, 2016): 12244–49. http://dx.doi.org/10.1073/pnas.1510227113.

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The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.
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Häkkinen, Antti, Juha Koiranen, Julia Casado, Katja Kaipio, Oskari Lehtonen, Eleonora Petrucci, Johanna Hynninen, et al. "qSNE: quadratic rate t-SNE optimizer with automatic parameter tuning for large datasets." Bioinformatics 36, no. 20 (July 14, 2020): 5086–92. http://dx.doi.org/10.1093/bioinformatics/btaa637.

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Abstract Motivation Non-parametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell datasets. Current implementations scale poorly to massive datasets and often require downsampling or interpolative approximations, which can leave less-frequent populations undiscovered and much information unexploited. Results We implemented a fast t-SNE package, qSNE, which uses a quasi-Newton optimizer, allowing quadratic convergence rate and automatic perplexity (level of detail) optimizer. Our results show that these improvements make qSNE significantly faster than regular t-SNE packages and enables full analysis of large datasets, such as mass cytometry data, without downsampling. Availability and implementation Source code and documentation are openly available at https://bitbucket.org/anthakki/qsne/. Supplementary information Supplementary data are available at Bioinformatics online.
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Schmitz, S., U. Weidner, H. Hammer, and A. Thiele. "EVALUATING UNIFORM MANIFOLD APPROXIMATION AND PROJECTION FOR DIMENSION REDUCTION AND VISUALIZATION OF POLINSAR FEATURES." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2021 (June 17, 2021): 39–46. http://dx.doi.org/10.5194/isprs-annals-v-1-2021-39-2021.

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Abstract. In this paper, the nonlinear dimension reduction algorithm Uniform Manifold Approximation and Projection (UMAP) is investigated to visualize information contained in high dimensional feature representations of Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data. Based on polarimetric parameters, target decomposition methods and interferometric coherences a wide range of features is extracted that spans the high dimensional feature space. UMAP is applied to determine a representation of the data in 2D and 3D euclidean space, preserving local and global structures of the data and still suited for classification. The performance of UMAP in terms of generating expressive visualizations is evaluated on PolInSAR data acquired by the F-SAR sensor and compared to that of Principal Component Analysis (PCA), Laplacian Eigenmaps (LE) and t-distributed Stochastic Neighbor embedding (t-SNE). For this purpose, a visual analysis of 2D embeddings is performed. In addition, a quantitative analysis is provided for evaluating the preservation of information in low dimensional representations with respect to separability of different land cover classes. The results show that UMAP exceeds the capability of PCA and LE in these regards and is competitive with t-SNE.
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Walsh, Joe, Ian Timothy Heazlewood, Mark DeBeliso, and Mike Climstein. "Application of t-distributed Stochastic Neighbor Embedding (t-SNE) to clustering of social affiliation and recognition psychological motivations in masters athletes." International Journal of Sport, Exercise and Health Research 4, no. 1 (May 31, 2020): 1–6. http://dx.doi.org/10.31254/sportmed.4101.

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An exploration of clustering of psychological motivations for participation in sport was conducted using t-distributed Stochastic Neighbor Embedding (t-SNE). The data source used for this investigation was survey data gathered on World Masters Games competitors using the Motivations of Marathoners Scales (MOMS). The aim of this research was to assess the suitability of applying t-SNE to creating two-dimensional scatter plots to visualise the relationship between different psychological motivators for the Social Motives category of the MOMS. Application of t-SNE plots could assist in visually mapping psychological constructs and gaining greater understanding of the underlying patterns in the MOMS tool. Although there was more disparity in the clustering of categories within Social Motives than was hypothesised, some clustering patterns were observed. Some items in the MOMS Social Motives category were connected in a logical manner that complied with those originally proposed by the developers of the MOMS. Two-dimensional scatter plots produced using t-SNE may assist in creating hypotheses about the relationships present between psychological constructs in such high-dimensional data.
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Meyer, Bruno Henrique, Aurora Trinidad Ramirez Pozo, and Wagner M. Nunan Zola. "Improving Barnes-Hut t-SNE Algorithm in Modern GPU Architectures with Random Forest KNN and Simulated Wide-Warp." ACM Journal on Emerging Technologies in Computing Systems 17, no. 4 (June 30, 2021): 1–26. http://dx.doi.org/10.1145/3447779.

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The t-Distributed Stochastic Neighbor Embedding (t-SNE) is a widely used technique for dimensionality reduction but is limited by its scalability when applied to large datasets. Recently, BH-tSNE was proposed; this is a successful approximation that transforms a step of the original algorithm into an N-Body simulation problem that can be solved by a modified Barnes-Hut algorithm. However, this improvement still has limitations to process large data volumes (millions of records). Late studies, such as t-SNE-CUDA, have used GPUs to implement highly parallel BH-tSNE. In this research we have developed a new GPU BH-tSNE implementation that produces the embedding of multidimensional data points into three-dimensional space. We examine scalability issues in two of the most expensive steps of GPU BH-tSNE by using efficient memory access strategies , recent acceleration techniques , and a new approach to compute the KNN graph structure used in BH-tSNE with GPU. Our design allows up to 460% faster execution when compared to the t-SNE-CUDA implementation. Although our SIMD acceleration techniques were used in a modern GPU setup, we have also verified a potential for applications in the context of multi-core processors.
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38

Schwarz, Christian, Rebecca Buchholz, Muhammad Jawad, Vanessa Hoesker, Claudia Terwesten-Solé, Uwe Karst, Lars Linsen, et al. "Fingerprints of Element Concentrations in Infective Endocarditis Obtained by Mass Spectrometric Imaging and t-Distributed Stochastic Neighbor Embedding." ACS Infectious Diseases 8, no. 2 (January 19, 2022): 360–72. http://dx.doi.org/10.1021/acsinfecdis.1c00485.

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39

Tao, Keyu, Jian Cao, Yuce Wang, Julei Mi, Wanyun Ma, and Chunhua Shi. "Chemometric Classification of Crude Oils in Complex Petroleum Systems Using t-Distributed Stochastic Neighbor Embedding Machine Learning Algorithm." Energy & Fuels 34, no. 5 (April 28, 2020): 5884–99. http://dx.doi.org/10.1021/acs.energyfuels.0c01333.

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40

Horn, Nils, Fabian Gampfer, and Rüdiger Buchkremer. "Latent Dirichlet Allocation and t-Distributed Stochastic Neighbor Embedding Enhance Scientific Reading Comprehension of Articles Related to Enterprise Architecture." AI 2, no. 2 (April 22, 2021): 179–94. http://dx.doi.org/10.3390/ai2020011.

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As the amount of scientific information increases steadily, it is crucial to improve fast-reading comprehension. To grasp many scientific articles in a short period, artificial intelligence becomes essential. This paper aims to apply artificial intelligence methodologies to examine broad topics such as enterprise architecture in scientific articles. Analyzing abstracts with latent dirichlet allocation or inverse document frequency appears to be more beneficial than exploring full texts. Furthermore, we demonstrate that t-distributed stochastic neighbor embedding is well suited to explore the degree of connectivity to neighboring topics, such as complexity theory. Artificial intelligence produces results that are similar to those obtained by manual reading. Our full-text study confirms enterprise architecture trends such as sustainability and modeling languages.
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41

Oliveira, Fábio Henrique M., Alessandro R. P. Machado, and Adriano O. Andrade. "On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease." Computational and Mathematical Methods in Medicine 2018 (November 4, 2018): 1–17. http://dx.doi.org/10.1155/2018/8019232.

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Parkinson’s disease (PD) is a neurodegenerative disorder that remains incurable. The available treatments for the disorder include pharmacologic therapies and deep brain stimulation (DBS). These approaches may cause distinct side effects and motor responses. This work presents the application of t-distributed stochastic neighbor embedding (t-SNE), which is a machine learning algorithm for nonlinear dimensionality reduction and data visualization, for the problem of discriminating neurologically healthy individuals from those suffering from PD (treated with levodopa and DBS). Furthermore, the assessment of classification methods is presented. Inertial and electromyographic data were collected while the subjects executed a sequence of four motor tasks. The results were focused on the comparison of the classification performance of a support vector machine (SVM) while discriminating two-dimensional feature sets estimated from Principal Component Analysis (PCA), Sammon’s mapping, and t-SNE. The results showed visual and statistical differences for all three investigated groups. Classification accuracy for PCA, Sammon’s mapping, and t-SNE was, respectively, 73.5%, 78.6%, and 96.9% for the training set and 67.8%, 74.1%, and 76.6% for the test set. The possibility of discriminating healthy individuals from those with PD treated with levodopa and DBS highlights the fact that each treatment method produces distinct motor behavior. The scatter plots resulting from t-SNE could be used in the clinical practice as an objective tool for measuring the discrepancy between normal and abnormal motor behaviors, being thus useful for the adjustment of treatments and the follow-up of the disorder.
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Senigagliesi, Linda, Gianluca Ciattaglia, Adelmo De Santis, and Ennio Gambi. "People Walking Classification Using Automotive Radar." Electronics 9, no. 4 (March 30, 2020): 588. http://dx.doi.org/10.3390/electronics9040588.

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Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.
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Bezrukov, N. S., and E. V. Polyanskaya. "CONSTRUCTION OF A DATA CLUSTERING MODEL EXEMPLIFIED BY DEMO-GRAPHIC INDICATORS OF THE FEFD REGIONS." Informatika i sistemy upravleniya, no. 4 (2021): 3–12. http://dx.doi.org/10.22250/isu.2021.70.3-12.

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The article deals with the problem of constructing a model for classifying the regions of the Far Eastern Federal District on the basis of demographic data with the use of machine learning algo-rithms - t-distributed Stochastic Neighbor Embedding, K-means and self-organizing networks. Column diagrams and heat maps of correlation coefficients are built for demographic indicators. It is proposed to replace demographic indicators with rank values. The effect it has on the classi-fication results is studied. The classifier has been built on the basis of a self-organizing network, that allows the regions of the Far Eastern Federal District to be classified as belonging to one of the classes: depressed, satisfactory or good.
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Han, Yongming, Shuang Liu, Di Cong, Zhiqiang Geng, Jinzhen Fan, Jingyang Gao, and Tingrui Pan. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes." Energy 225 (June 2021): 120255. http://dx.doi.org/10.1016/j.energy.2021.120255.

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Et al., Hariharan S. "Analysing Effect of t-SNE and 1-D CNN on Performance of Hyperspectral Image Classification." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 5, 2021): 1828–33. http://dx.doi.org/10.17762/turcomat.v12i6.4166.

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Feature extraction is a crucial step in Hyperspectral Image classification that aids in processing data effectively without losing relevant information. This step is essential when dealing with images with high dimensions because they suffer from Hughes phenomenon or the curse of high dimensionality. This phenomenon occurs in high dimensional datasets where the number of training samples is limited. In this paper, we have studied the influence of feature extraction techniques in HSI classification. We have compared the efficiency of three widely used techniques, namely Principal Component Analysis, t- Stochastic Neighbor Embedding and Convolutional Neural Network. Overall classification accuracy for PCA when used with KNN, a commonly used classification algorithm was found to be 69.79% while t-SNE with KNN was 71.04%. When CNN was used for feature extraction, its outperformed t-SNE and PCA with a wide margin with classification accuracy reaching as high as 95.06%.
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Wang, Yuliang, Huiyi Su, and Mingshi Li. "An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images." Remote Sensing 11, no. 2 (January 11, 2019): 136. http://dx.doi.org/10.3390/rs11020136.

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Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs.
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Liu, Xiaoyuan, Senxiang Lu, Yan Ren, and Zhenning Wu. "Wind Turbine Anomaly Detection Based on SCADA Data Mining." Electronics 9, no. 5 (May 2, 2020): 751. http://dx.doi.org/10.3390/electronics9050751.

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In this paper, a wind turbine anomaly detection method based on a generalized feature extraction is proposed. Firstly, wind turbine (WT) attributes collected from the Supervisory Control And Data Acquisition (SCADA) system are clustered with k-means, and the Silhouette Coefficient (SC) is adopted to judge the effectiveness of clustering. Correlation between attributes within a class becomes larger, correlation between classes becomes smaller by clustering. Then, dimensions of attributes within classes are reduced based on t-Distributed-Stochastic Neighbor Embedding (t-SNE) so that the low-dimensional attributes can be more full and more concise in reflecting the WT attributes. Finally, the detection model is trained and the normal or abnormal state is detected by the classification result 0 or 1 respectively. Experiments consists of three cases with SCADA data demonstrate the effectiveness of the proposed method.
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Kiran, Mariam, Scott Campbell, Fatema Bannat Wala, Nick Buraglio, and Inder Monga. "Machine learning-based analysis of COVID-19 pandemic impact on US research networks." ACM SIGCOMM Computer Communication Review 51, no. 4 (October 24, 2021): 23–35. http://dx.doi.org/10.1145/3503954.3503958.

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This study explores how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-at-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.
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Sonnewald, Maike, Stephanie Dutkiewicz, Christopher Hill, and Gael Forget. "Elucidating ecological complexity: Unsupervised learning determines global marine eco-provinces." Science Advances 6, no. 22 (May 2020): eaay4740. http://dx.doi.org/10.1126/sciadv.aay4740.

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An unsupervised learning method is presented for determining global marine ecological provinces (eco-provinces) from plankton community structure and nutrient flux data. The systematic aggregated eco-province (SAGE) method identifies eco-provinces within a highly nonlinear ecosystem model. To accommodate the non-Gaussian covariance of the data, SAGE uses t-stochastic neighbor embedding (t-SNE) to reduce dimensionality. Over a hundred eco-provinces are identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Using a connectivity graph with ecological dissimilarity as the distance metric, robust aggregated eco-provinces (AEPs) are objectively defined by nesting the eco-provinces. Using the AEPs, the control of nutrient supply rates on community structure is explored. Eco-provinces and AEPs are unique and aid model interpretation. They could facilitate model intercomparison and potentially improve understanding and monitoring of marine ecosystems.
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Liu, Xiaobo, Hantao Guo, and Yibing Liu. "One-Shot Fault Diagnosis of Wind Turbines Based on Meta-Analogical Momentum Contrast Learning." Energies 15, no. 9 (April 25, 2022): 3133. http://dx.doi.org/10.3390/en15093133.

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The rapid development of artificial intelligence offers more opportunities for intelligent mechanical diagnosis. Fault diagnosis of wind turbines is beneficial to improve the reliability of wind turbines. Due to various reasons, such as difficulty in obtaining fault data, random changes in operating conditions, or compound faults, many deep learning algorithms show poor performance. When fault samples are small, ordinary deep learning will fall into overfitting. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples. A novel method based on meta-analogical momentum contrast learning (MA-MOCO) is proposed in this paper to solve the problem of the very few samples of wind turbine failures, especially one-shot. By improving the momentum contrast learning (MOCO) and using the training idea of meta-learning, the one-shot fault diagnosis of wind turbine drivetrain is analyzed. The proposed model shows a higher accuracy than other common models (e.g., model-agnostic meta-learning and Siamese net) in one-shot learning. The feature embedding is visualized by t-distributed stochastic neighbor embedding (t-SNE) in order to test the effectiveness of the proposed model.
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