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 99 articles , 2 standard articles )

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  1. Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
  2. 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)
  3. Gao, Tingran; Brodzki, Jacek; Mukherjee, Sayan: The geometry of synchronization problems and learning group actions (2021)
  4. Han, Xinyu; Zhao, Yi; Small, Michael: Revisiting the memory capacity in reservoir computing of directed acyclic network (2021)
  5. 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)
  6. Schoen, Fabio; Tigli, Luca: Efficient large scale global optimization through clustering-based population methods (2021)
  7. Guan, Leying; Chen, Xi; Hung Wong, Wing: Detecting strong signals in gene perturbation experiments: an adaptive approach with power guarantee and FDR control (2020)
  8. Hoiles, William; Krishnamurthy, Vikram; Pattanayak, Kunal: Rationally inattentive inverse reinforcement learning explains YouTube commenting behavior (2020)
  9. Horenko, Illia: On a scalable entropic breaching of the overfitting barrier for small data problems in machine learning (2020)
  10. Jaffe, Ariel; Kluger, Yuval; Linderman, George C.; Mishne, Gal; Steinerberger, Stefan: Randomized near-neighbor graphs, giant components and applications in data science (2020)
  11. Keshavarzzadeh, Vahid; Kirby, Robert M.; Narayan, Akil: Stress-based topology optimization under uncertainty via simulation-based Gaussian process (2020)
  12. Lang, Rongling; Lu, Ruibo; Zhao, Chenqian; Qin, Honglei; Liu, Guodong: Graph-based semi-supervised one class support vector machine for detecting abnormal lung sounds (2020)
  13. Lee, Kookjin; Carlberg, Kevin T.: Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (2020)
  14. Lee, O-Joun; Jung, Jason J.: Story embedding: learning distributed representations of stories based on character networks (2020)
  15. Lv, Shaoqing; Xiang, Ju; Feng, Jingyu; Wang, Honggang; Lu, Guangyue; Li, Min: Community enhancement network embedding based on edge reweighting preprocessing (2020)
  16. Philippe Boileau, Nima Hejazi, Sandrine Dudoit: scPCA: A toolbox for sparse contrastive principal component analysis in R (2020) not zbMATH
  17. Ruiz, Francisco J. R.; Athey, Susan; Blei, David M.: SHOPPER: a probabilistic model of consumer choice with substitutes and complements (2020)
  18. Shi, Min; Tang, Yufei; Zhu, Xingquan; Liu, Jianxun; He, Haibo: Topical network embedding (2020)
  19. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  20. Bugbee, Bruce; Bush, Brian W.; Gruchalla, Kenny; Potter, Kristin; Brunhart-lupo, Nicholas; Krishnan, Venkat: Enabling immersive engagement in energy system models with deep learning (2019)

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