Visualizing Data using t-SNE. We present a new technique called ”t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images ofobjects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large data sets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of data sets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the data sets.

References in zbMATH (referenced in 112 articles , 2 standard articles )

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  1. Blanchard, Antoine; Sapsis, Themistoklis: Output-weighted optimal sampling for Bayesian experimental design and uncertainty quantification (2021)
  2. Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
  3. Chang, Der-Chen; Frieder, Ophir; Hung, Chi-Feng; Yao, Hao-Ren: The analysis from nonlinear distance metric to kernel-based prescription prediction system (2021)
  4. Chao, Xiangrui; Kou, Gang; Peng, Yi; Viedma, Enrique Herrera: Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: an application in financial inclusion (2021)
  5. Cheng, Lu; Varshney, Kush R.; Liu, Huan: Socially responsible AI algorithms: issues, purposes, and challenges (2021)
  6. Cheng, Yichen; Wang, Xinlei; Xia, Yusen: Supervised (t)-distributed stochastic neighbor embedding for data visualization and classification (2021)
  7. Coimbra, Danilo B.; Martins, Rafael M.; Mota, Edson; Tiburtino, Tacito; Diamantino, Pedro; Peixoto, Maycon L. M.: Analyzing the quality of local and global multidimensional projections using performance evaluation planning (2021)
  8. Gao, Tingran; Brodzki, Jacek; Mukherjee, Sayan: The geometry of synchronization problems and learning group actions (2021)
  9. Han, Xinyu; Zhao, Yi; Small, Michael: Revisiting the memory capacity in reservoir computing of directed acyclic network (2021)
  10. Ji, Zhicheng; Ji, Hongkai: Discussion of “Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data” (2021)
  11. Jurewicz, Mateusz; Derczynski, Leon: Set-to-sequence methods in machine learning: a review (2021)
  12. Kao, Yu-Wei; Chen, Hung-Hsuan: Associated learning: decomposing end-to-end backpropagation based on autoencoders and target propagation (2021)
  13. Kileel, Joe; Moscovich, Amit; Zelesko, Nathan; Singer, Amit: Manifold learning with arbitrary norms (2021)
  14. Landa, Boris; Coifman, Ronald R.; Kluger, Yuval: Doubly stochastic normalization of the Gaussian kernel is robust to heteroskedastic noise (2021)
  15. Leung, Raymond; Balamurali, Mehala; Melkumyan, Arman: Sample truncation strategies for outlier removal in geochemical data: the MCD robust distance approach versus t-SNE ensemble clustering (2021)
  16. Lin, Kevin Z.; Lei, Jing; Roeder, Kathryn: Exponential-family embedding with application to cell developmental trajectories for single-cell RNA-seq data (2021)
  17. Schoen, Fabio; Tigli, Luca: Efficient large scale global optimization through clustering-based population methods (2021)
  18. Wang, Minjie; Allen, Genevera I.: Integrative generalized convex clustering optimization and feature selection for mixed multi-view data (2021)
  19. Wu, Mike; Parbhoo, Sonali; Hughes, Michael C.; Roth, Volker; Doshi-Velez, Finale: Optimizing for interpretability in deep neural networks with tree regularization (2021)
  20. Comin, Cesar H.; Peron, Thomas; Silva, Filipi N.; Amancio, Diego R.; Rodrigues, Francisco A.; Costa, Luciano da F.: Complex systems: features, similarity and connectivity (2020)

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