Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
This is an incremental work that serves as an educational resource for researchers and practitioners in machine learning interested in dimensionality reduction techniques.
The paper provides a tutorial and survey on Stochastic Neighbor Embedding (SNE) and its variants, explaining their probabilistic approaches for manifold learning and dimensionality reduction, but does not present new experimental results or concrete numbers.
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.