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 159 articles , 2 standard articles )

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  1. dos Santos, Ketson R.; Giovanis, Dimitrios G.; Shields, Michael D.: Grassmannian diffusion maps-based dimension reduction and classification for high-dimensional data (2022)
  2. Fanuel, Michaël; Aspeel, Antoine; Delvenne, Jean-Charles; Suykens, Johan A. K.: Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions (2022)
  3. Gao, Jia-Xing; Wang, Zhen-Yi; Zhang, Michael Q.; Qian, Min-Ping; Jiang, Da-Quan: A data-driven method to learn a jump diffusion process from aggregate biological gene expression data (2022)
  4. Jones, Corinne; Roulet, Vincent; Harchaoui, Zaid: Discriminative clustering with representation learning with any ratio of labeled to unlabeled data (2022)
  5. Linderman, George C.; Steinerberger, Stefan: Dimensionality reduction via dynamical systems: the case of t-SNE (2022)
  6. Little, Anna; McKenzie, Daniel; Murphy, James M.: Balancing geometry and density: path distances on high-dimensional data (2022)
  7. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  8. Bej, Saptarshi; Davtyan, Narek; Wolfien, Markus; Nassar, Mariam; Wolkenhauer, Olaf: LoRAS: an oversampling approach for imbalanced datasets (2021)
  9. Bernardo, Lucas Salvador; Damaševičius, Robertas; de Albuquerque, Victor Hugo C.; Maskeliūnas, Rytis: A hybrid two-stage squeezenet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns (2021)
  10. Blanchard, Antoine; Sapsis, Themistoklis: Output-weighted optimal sampling for Bayesian experimental design and uncertainty quantification (2021)
  11. Boubekki, Ahcène; Kampffmeyer, Michael; Brefeld, Ulf; Jenssen, Robert: Joint optimization of an autoencoder for clustering and embedding (2021)
  12. Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
  13. Chang, Der-Chen; Frieder, Ophir; Hung, Chi-Feng; Yao, Hao-Ren: The analysis from nonlinear distance metric to kernel-based prescription prediction system (2021)
  14. 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)
  15. Cheng, Lu; Varshney, Kush R.; Liu, Huan: Socially responsible AI algorithms: issues, purposes, and challenges (2021)
  16. Cheng, Yichen; Wang, Xinlei; Xia, Yusen: Supervised (t)-distributed stochastic neighbor embedding for data visualization and classification (2021)
  17. Chen, Jiaoyan; Hu, Pan; Jimenez-Ruiz, Ernesto; Holter, Ole Magnus; Antonyrajah, Denvar; Horrocks, Ian: \textttOWL2Vec*: embedding of OWL ontologies (2021)
  18. Chen, Jiyu; Guo, Yiwen; Zheng, Qianjun; Chen, Hao: Protect privacy of deep classification networks by exploiting their generative power (2021)
  19. 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)
  20. Deswal, Sumit; Bulusu, Krishna C.; Agapow, Paul-Michael; Khan, Faisal M.: Precision medicine (2021)

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