T-distributed stochastic neighbor embedding: Difference between revisions

Revision as of 19:22, 30 May 2014

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'''t-Distributeddistributed Stochasticstochastic Neighborneighbor Embeddingembedding (t-SNE)''' is a [[machine learning]] algorithm for [[dimensionality reduction]] developed by Laurens van der Maaten and [[Geoffrey Hinton]].<ref>{{cite journal|last=van der Maaten|first=L.J.P.|coauthors=Hinton, G.E.|title=Visualizing High-Dimensional Data Using t-SNE|journal=Journal of Machine Learning Research 9|date=Nov 2008|pages=2579–2605|url=http://jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf}}</ref> It is a [[nonlinear dimensionality reduction]] technique that is particularly well suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot. 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.

The t-SNE algorithms comprises two main stages. First, t-SNE constructs a [[probability distribution]] over pairs of high-dimensional objects in such a way that similar objects have a high probability of being picked, whilst dissimilar points have an [[infinitesimal]] probability of being picked. Second, t-SNE defines a similar probability distribution over the points in the low-dimensional map, and it minimizes the [[Kullback–Leibler divergence]] between the two distributions with respect to the locations of the points in the map.