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.
t-SNE has been used in a wide range of applications, including [[computer security]] research,<ref>{{cite journal|last=Gashi|first=I.|coauthorsauthor2=Stankovic, V., |author3=Leita, C., |author4=Thonnard, O. |title=An Experimental Study of Diversity with Off-the-shelf AntiVirus Engines|journal=Proceedings of the IEEE International Symposium on Network Computing and Applications|year=2009|pages=4–11}}</ref> [[music analysis]],<ref>{{cite journal|last=Hamel|first=P.|author2=Eck, D. |title=Learning Features from Music Audio with Deep Belief Networks|journal=Proceedings of the International Society for Music Information Retrieval Conference|year=2010|pages=339–344}}</ref> [[cancer research]],<ref>{{cite journal|last=Jamieson|first=A.R.|coauthorsauthor2=Giger, M.L., |author3=Drukker, K., |author4=Lui, H., |author5=Yuan, Y., |author6=Bhooshan, N. |title=Exploring Nonlinear Feature Space Dimension Reduction and Data Representation in Breast CADx with Laplacian Eigenmaps and t-SNE|journal=Medical Physics 37(1)|year=2010|pages=339–351|doi=10.1118/1.3267037|volume=37}}</ref> and [[bio-informatics]].<ref>{{cite journal|last=Wallach|first=I.|author2=Liliean, R. |title=The Protein-Small-Molecule Database, A Non-Redundant Structural Resource for the Analysis of Protein-Ligand Binding|journal=Bioinformatics 25(5)|year=2009|pages=615–620|doi=10.1093/bioinformatics/btp035|volume=25|issue=5}}</ref>
