Editing Nonlinear dimensionality reduction (section)

=== t-distributed stochastic neighbor embedding ===
<!-- This paragraph should be moved to the general class of stochastic neighbor embedding techniques. -->

[[t-distributed stochastic neighbor embedding]] (t-SNE)<ref>{{cite journal|last1=van der Maaten|first1=L.J.P.|last2=Hinton |first2=G.E. |title=Visualizing High-Dimensional Data Using t-SNE|journal=Journal of Machine Learning Research |volume=9|date=2008|pages=2579–2605|url=http://jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf}}</ref> is widely used. It is one of a family of stochastic neighbor embedding methods. The algorithm computes the probability that pairs of datapoints in the high-dimensional space are related, and then chooses low-dimensional embeddings which produce a similar distribution.