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.

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  1. Baptista, Marcia L.; Goebel, Kai; Henriques, Elsa M. P.: Relation between prognostics predictor evaluation metrics and local interpretability SHAP values (2022)
  2. dos Santos, Ketson R.; Giovanis, Dimitrios G.; Shields, Michael D.: Grassmannian diffusion maps-based dimension reduction and classification for high-dimensional data (2022)
  3. Fanuel, Michaël; Aspeel, Antoine; Delvenne, Jean-Charles; Suykens, Johan A. K.: Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions (2022)
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  6. Jones, Corinne; Roulet, Vincent; Harchaoui, Zaid: Discriminative clustering with representation learning with any ratio of labeled to unlabeled data (2022)
  7. Komarichev, Artem; Hua, Jing; Zhong, Zichun: Learning geometry-aware joint latent space for simultaneous multimodal shape generation (2022)
  8. Linderman, George C.; Steinerberger, Stefan: Dimensionality reduction via dynamical systems: the case of t-SNE (2022)
  9. Little, Anna; McKenzie, Daniel; Murphy, James M.: Balancing geometry and density: path distances on high-dimensional data (2022)
  10. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  11. Škrlj, Blaž; Džeroski, Sašo; Lavrač, Nada; Petković, Matej: ReliefE: feature ranking in high-dimensional spaces via manifold embeddings (2022)
  12. Yao, Kaixuan; Liang, Jiye; Liang, Jianqing; Li, Ming; Cao, Feilong: Multi-view graph convolutional networks with attention mechanism (2022)
  13. Bej, Saptarshi; Davtyan, Narek; Wolfien, Markus; Nassar, Mariam; Wolkenhauer, Olaf: LoRAS: an oversampling approach for imbalanced datasets (2021)
  14. 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)
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  16. Boubekki, Ahcène; Kampffmeyer, Michael; Brefeld, Ulf; Jenssen, Robert: Joint optimization of an autoencoder for clustering and embedding (2021)
  17. Burkart, Nadia; Huber, Marco F.: A survey on the explainability of supervised machine learning (2021)
  18. Chang, Der-Chen; Frieder, Ophir; Hung, Chi-Feng; Yao, Hao-Ren: The analysis from nonlinear distance metric to kernel-based prescription prediction system (2021)
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