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.
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2019-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-13055-y |
| Summary: | 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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| ISSN: | 2041-1723 |