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: | , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2019
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/e5126a248205487bb5a7c54c11c0bcc3 |
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| Résumé: | 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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