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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.07746 |
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| _version_ | 1866912936250834944 |
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| author | Bergam, Noah Snoeck, Szymon Verma, Nakul |
| author_facet | Bergam, Noah Snoeck, Szymon Verma, Nakul |
| contents | Central to the widespread use of t-distributed stochastic neighbor embedding (t-SNE) is the conviction that it produces visualizations whose structure roughly matches that of the input. To the contrary, we prove that (1) the strength of the input clustering, and (2) the extremity of outlier points, cannot be reliably inferred from the t-SNE output. We demonstrate the prevalence of these failure modes in practice as well. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07746 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | t-SNE Exaggerates Clusters, Provably Bergam, Noah Snoeck, Szymon Verma, Nakul Machine Learning Central to the widespread use of t-distributed stochastic neighbor embedding (t-SNE) is the conviction that it produces visualizations whose structure roughly matches that of the input. To the contrary, we prove that (1) the strength of the input clustering, and (2) the extremity of outlier points, cannot be reliably inferred from the t-SNE output. We demonstrate the prevalence of these failure modes in practice as well. |
| title | t-SNE Exaggerates Clusters, Provably |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.07746 |