Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

Visualisation tools that use dimensionality reduction, such as t-SNE, provide poor visualisation on large data sets of millions of observations. Here the authors present opt-SNE, that automatically finds data set-tailored parameters for t-SNE to optimise visualisation and improve analysis.

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Auteurs principaux:	Anna C. Belkina, Christopher O. Ciccolella, Rina Anno, Richard Halpert, Josef Spidlen, Jennifer E. Snyder-Cappione
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2019
Sujets:	Science Q
Accès en ligne:	https://doaj.org/article/e5126a248205487bb5a7c54c11c0bcc3
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id	oai:doaj.org-article:e5126a248205487bb5a7c54c11c0bcc3
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spelling	oai:doaj.org-article:e5126a248205487bb5a7c54c11c0bcc32021-12-02T15:35:57ZAutomated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets10.1038/s41467-019-13055-y2041-1723https://doaj.org/article/e5126a248205487bb5a7c54c11c0bcc32019-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13055-yhttps://doaj.org/toc/2041-1723Visualisation tools that use dimensionality reduction, such as t-SNE, provide poor visualisation on large data sets of millions of observations. Here the authors present opt-SNE, that automatically finds data set-tailored parameters for t-SNE to optimise visualisation and improve analysis.Anna C. BelkinaChristopher O. CiccolellaRina AnnoRichard HalpertJosef SpidlenJennifer E. Snyder-CappioneNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-12 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Anna C. Belkina Christopher O. Ciccolella Rina Anno Richard Halpert Josef Spidlen Jennifer E. Snyder-Cappione Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
description	Visualisation tools that use dimensionality reduction, such as t-SNE, provide poor visualisation on large data sets of millions of observations. Here the authors present opt-SNE, that automatically finds data set-tailored parameters for t-SNE to optimise visualisation and improve analysis.
format	article
author	Anna C. Belkina Christopher O. Ciccolella Rina Anno Richard Halpert Josef Spidlen Jennifer E. Snyder-Cappione
author_facet	Anna C. Belkina Christopher O. Ciccolella Rina Anno Richard Halpert Josef Spidlen Jennifer E. Snyder-Cappione
author_sort	Anna C. Belkina
title	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_short	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_full	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_fullStr	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_full_unstemmed	Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
title_sort	automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/e5126a248205487bb5a7c54c11c0bcc3
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Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

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