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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 12

Code Repositories

SNE-tSNE

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


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.