This algorithm, proposed by Van der Mateen and Hinton and formally known as t-Distributed Stochastic Neighbor Embedding (t-SNE), is one of the most powerful manifold dimensionality reduction techniques. Contrary to the other methods, this algorithm starts with a fundamental assumption: the similarity between two N-dimensional points xi and xj can be represented as the conditional probability p(xj|xi) where each point is represented by a Gaussian distribution centered in xi and with variance σi. The variances are selected starting from the desired perplexity, defined as:
Low-perplexity values indicate a low uncertainty, and are normally ...
