Heavy-Tailed Symmetric Stochastic Neigbor Embedding

by Zhirong Yang, Irwin King, Zenglin Xu and Erkki Oja

t-SNE is a very useful visualization algorithm, which inherits the idea from SNE but modifies the neighborhood probabilities to t-distributions instead of the original. This heavy-tailed distribution works pretty well on many data. This paper discusses a more general case.

After transforming the original problem into its Lagrange dual, the authors get a unified algorithm (fixed-point iteration) for a family of heavy tailed distribution (including t-distribution). This is somewhat not as interesting as I have expected.

The authors also discussed how to integrate supervised information into the SNE algorithm. But their idea is kind of rough: inserting a similarity computed from the supervised information into the similarity in the high-dimensional space. I don't know whether this would truly work...