Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey

09/22/2020 ∙ by Benyamin Ghojogh, et al. ∙ 0

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space. SNE considers Gaussian distribution for the probability in both the input and embedding spaces. However, t-SNE uses the Student-t and Gaussian distributions in these spaces, respectively. In this tutorial and survey paper, we explain SNE, symmetric SNE, t-SNE (or Cauchy-SNE), and t-SNE with general degrees of freedom. We also cover the out-of-sample extension and acceleration for these methods. Some simulations to visualize the embeddings are also provided.



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Code Repositories


The codes for Stochastic Neighbor Embedding (SNE), t-SNE, and their variants.

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