Content deleted Content added
sp, date & link fixes; unlinking common words, replaced: Kullback-Leibler divergence → Kullback–Leibler divergence (3) using AWB |
{{primary sources}} is not appropriate, article has references to well-cited, peer reviewed publications |
||
Line 1:
{{lower case title}}
'''t-Distributed Stochastic Neighbor Embedding (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.
| |||