  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.
