T-Distributed Stochastic Neighbor Embedding

t-distributed stochastic neighbor embedding ( t-SNE ) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map. It is based on Stochastic Neighbor Embedding originally developed by Geoffrey Hinton and Sam Roweis, where Laurens van der Maaten proposed the t -distributed variant. It is a nonlinear dimensionality reduction technique for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. Specifically, it models each high-dimensional object by a two- or three- dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.