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The t-SNE algorithm comprises two main stages. First, t-SNE constructs a [[probability distribution]] over pairs of high-dimensional objects in such a way that similar objects are assigned a higher probability while dissimilar points are assigned a lower probability. Second, t-SNE defines a similar probability distribution over the points in the low-dimensional map, and it minimizes the [[Kullback–Leibler divergence]] (KL divergence) between the two distributions with respect to the locations of the points in the map. While the original algorithm uses the [[Euclidean distance]] between objects as the base of its similarity metric, this can be changed as appropriate.
t-SNE has been used for visualization in a wide range of applications, including [[genomics]], [[computer security]] research,<ref>{{cite journal|last=Gashi|first=I.|author2=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> [[natural language processing]], [[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.|author2=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 |issue=1|year=2010|pages=339–351|doi=10.1118/1.3267037|pmid=20175497|volume=37|pmc=2807447}}</ref> [[bioinformatics]],<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 |year=2009|pages=615–620|doi=10.1093/bioinformatics/btp035|volume=25|issue=5|pmid=19153135|doi-access=free}}</ref> geological ___domain interpretation,<ref>{{Cite journal|date=2019-04-01|title=A comparison of t-SNE, SOM and SPADE for identifying material type domains in geological data|url=https://www.sciencedirect.com/science/article/pii/S0098300418306010|journal=Computers & Geosciences|language=en|volume=125|pages=78–89|doi=10.1016/j.cageo.2019.01.011|issn=0098-3004|last1=Balamurali|first1=Mehala|last2=Silversides|first2=Katherine L.|last3=Melkumyan|first3=Arman|s2cid=67926902}}</ref><ref>{{Cite journal|last1=Balamurali|first1=Mehala|last2=Melkumyan|first2=Arman|date=2016|editor-last=Hirose|editor-first=Akira|editor2-last=Ozawa|editor2-first=Seiichi|editor3-last=Doya|editor3-first=Kenji|editor4-last=Ikeda|editor4-first=Kazushi|editor5-last=Lee|editor5-first=Minho|editor6-last=Liu|editor6-first=Derong|title=t-SNE Based Visualisation and Clustering of Geological Domain|url=https://link.springer.com/chapter/10.1007/978-3-319-46681-1_67|journal=Neural Information Processing|series=Lecture Notes in Computer Science|volume=9950|language=en|___location=Cham|publisher=Springer International Publishing|pages=565–572|doi=10.1007/978-3-319-46681-1_67|isbn=978-3-319-46681-1}}</ref><ref>{{Cite journal|last1=Leung|first1=Raymond|last2=Balamurali|first2=Mehala|last3=Melkumyan|first3=Arman|date=2021-01-01|title=Sample Truncation Strategies for Outlier Removal in Geochemical Data: The MCD Robust Distance Approach Versus t-SNE Ensemble Clustering|url=https://doi.org/10.1007/s11004-019-09839-z|journal=Mathematical Geosciences|language=en|volume=53|issue=1|pages=105–130|doi=10.1007/s11004-019-09839-z|s2cid=208329378|issn=1874-8953}}</ref> and biomedical signal processing.<ref>{{Cite book|last1=Birjandtalab|first1=J.|last2=Pouyan|first2=M. B.|last3=Nourani|first3=M.|date=2016-02-01|title=Nonlinear dimension reduction for EEG-based epileptic seizure detection|journal=2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)|pages=595–598|doi=10.1109/BHI.2016.7455968|isbn=978-1-5090-2455-1|s2cid=8074617}}</ref>
While t-SNE plots often seem to display [[cluster analysis|clusters]], the visual clusters can be influenced strongly by the chosen parameterization and therefore a good understanding of the parameters for t-SNE is necessary. Such "clusters" can be shown to even appear in non-clustered data,<ref>{{Cite web|url=https://stats.stackexchange.com/a/264647|title=K-means clustering on the output of t-SNE|website=Cross Validated|access-date=2018-04-16}}</ref> and thus may be false findings. Interactive exploration may thus be necessary to choose parameters and validate results.<ref>{{Cite journal|last1=Pezzotti|first1=Nicola|last2=Lelieveldt|first2=Boudewijn P. F.|last3=Maaten|first3=Laurens van der|last4=Hollt|first4=Thomas|last5=Eisemann|first5=Elmar|last6=Vilanova|first6=Anna|date=2017-07-01|title=Approximated and User Steerable tSNE for Progressive Visual Analytics|journal=IEEE Transactions on Visualization and Computer Graphics|language=en-US|volume=23|issue=7|pages=1739–1752|doi=10.1109/tvcg.2016.2570755|pmid=28113434|issn=1077-2626|arxiv=1512.01655|s2cid=353336}}</ref><ref>{{cite web|url=https://distill.pub/2016/misread-tsne/|title=How to Use t-SNE Effectively|last1=Wattenberg|first1=Martin|last2=Viégas|first2=Fernanda|date=2016-10-13|publisher=Distill|language=en|access-date=4 December 2017|last3=Johnson|first3=Ian}}</ref> It has been demonstrated that t-SNE is often able to recover well-separated clusters, and with special parameter choices, approximates a simple form of [[spectral clustering]].<ref>{{cite arXiv|last1=Linderman|first1=George C.|last2=Steinerberger|first2=Stefan|date=2017-06-08|title=Clustering with t-SNE, provably|eprint=1706.02582|class=cs.LG}}</ref>
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