t-SNE

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 88 articles , 2 standard articles )

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  1. Horenko, Illia: On a scalable entropic breaching of the overfitting barrier for small data problems in machine learning (2020)
  2. Jaffe, Ariel; Kluger, Yuval; Linderman, George C.; Mishne, Gal; Steinerberger, Stefan: Randomized near-neighbor graphs, giant components and applications in data science (2020)
  3. Keshavarzzadeh, Vahid; Kirby, Robert M.; Narayan, Akil: Stress-based topology optimization under uncertainty via simulation-based Gaussian process (2020)
  4. 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)
  5. Lee, O-Joun; Jung, Jason J.: Story embedding: learning distributed representations of stories based on character networks (2020)
  6. Philippe Boileau, Nima Hejazi, Sandrine Dudoit: scPCA: A toolbox for sparse contrastive principal component analysis in R (2020) not zbMATH
  7. Ruiz, Francisco J. R.; Athey, Susan; Blei, David M.: SHOPPER: a probabilistic model of consumer choice with substitutes and complements (2020)
  8. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  9. Bekkouch, Imad Eddine Ibrahim; Youssry, Youssef; Gafarov, Rustam; Khan, Adil; Khattak, Asad Masood: Triplet loss network for unsupervised domain adaptation (2019)
  10. 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)
  11. Cai, Hongmin; Huang, Qinjian; Rong, Wentao; Song, Yan; Li, Jiao; Wang, Jinhua; Chen, Jiazhou; Li, Li: Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms (2019)
  12. Chen, Mingjia; Zou, Qianfang; Wang, Changbo; Liu, Ligang: EdgeNet: deep metric learning for 3D shapes (2019)
  13. Chien, Vincent S. C.; Maess, Burkhard; Knösche, Thomas R.: A generic deviance detection principle for cortical on/off responses, omission response, and mismatch negativity (2019)
  14. Da, Qiaobo; Cheng, Jieren; Li, Qian; Zhao, Wentao: Socially-attentive representation learning for cold-start fraud review detection (2019)
  15. Genctav, Asli; Tari, Sibel: Discrepancy: local/global shape characterization with a roundness bias (2019)
  16. Han, Huimei; Li, Ying; Zhu, Xingquan: Convolutional neural network learning for generic data classification (2019)
  17. Hill, Mitch; Nijkamp, Erik; Zhu, Song-Chun: Building a telescope to look into high-dimensional image spaces (2019)
  18. Ibañez, R.; Abisset-Chavanne, E.; Cueto, E.; Ammar, A.; Duval, J. -L.; Chinesta, F.: Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction (2019)
  19. Li, Bo; Fan, Zhang-Tao; Zhang, Xiao-Long; Huang, De-Shuang: Robust dimensionality reduction via feature space to feature space distance metric learning (2019)
  20. Liu, Yiyi; Warren, Joshua L.; Zhao, Hongyu: A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data (2019)

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