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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Autores principales: Anna C. Belkina, Christopher O. Ciccolella, Rina Anno, Richard Halpert, Josef Spidlen, Jennifer E. Snyder-Cappione
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://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
work_keys_str_mv AT annacbelkina automatedoptimizedparametersfortdistributedstochasticneighborembeddingimprovevisualizationandanalysisoflargedatasets
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AT rinaanno automatedoptimizedparametersfortdistributedstochasticneighborembeddingimprovevisualizationandanalysisoflargedatasets
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AT josefspidlen automatedoptimizedparametersfortdistributedstochasticneighborembeddingimprovevisualizationandanalysisoflargedatasets
AT jenniferesnydercappione automatedoptimizedparametersfortdistributedstochasticneighborembeddingimprovevisualizationandanalysisoflargedatasets
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