Some Theoretical Limitations of t-SNE
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866908964977901568 |
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| author | Li, Rupert Mossel, Elchanan |
| author_facet | Li, Rupert Mossel, Elchanan |
| contents | t-SNE has gained popularity as a dimension reduction technique, especially for visualizing data. It is well-known that all dimension reduction techniques may lose important features of the data. We provide a mathematical framework for understanding this loss for t-SNE by establishing a number of results in different scenarios showing how important features of data are lost by using t-SNE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13295 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Some Theoretical Limitations of t-SNE Li, Rupert Mossel, Elchanan Machine Learning Probability t-SNE has gained popularity as a dimension reduction technique, especially for visualizing data. It is well-known that all dimension reduction techniques may lose important features of the data. We provide a mathematical framework for understanding this loss for t-SNE by establishing a number of results in different scenarios showing how important features of data are lost by using t-SNE. |
| title | Some Theoretical Limitations of t-SNE |
| topic | Machine Learning Probability |
| url | https://arxiv.org/abs/2604.13295 |