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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.15929 |
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| _version_ | 1866916911914156032 |
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| author | de Bodt, Cyril Diaz-Papkovich, Alex Bleher, Michael Bunte, Kerstin Coupette, Corinna Damrich, Sebastian Sanmartin, Enrique Fita Hamprecht, Fred A. Horvát, Emőke-Ágnes Kohli, Dhruv Krishnaswamy, Smita Lee, John A. Lelieveldt, Boudewijn P. F. McInnes, Leland Nabney, Ian T. Noichl, Maximilian Poličar, Pavlin G. Rieck, Bastian Wolf, Guy Mishne, Gal Kobak, Dmitry |
| author_facet | de Bodt, Cyril Diaz-Papkovich, Alex Bleher, Michael Bunte, Kerstin Coupette, Corinna Damrich, Sebastian Sanmartin, Enrique Fita Hamprecht, Fred A. Horvát, Emőke-Ágnes Kohli, Dhruv Krishnaswamy, Smita Lee, John A. Lelieveldt, Boudewijn P. F. McInnes, Leland Nabney, Ian T. Noichl, Maximilian Poličar, Pavlin G. Rieck, Bastian Wolf, Guy Mishne, Gal Kobak, Dmitry |
| contents | Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis is now greater than ever. In recent years, numerous embedding algorithms have been developed, and their usage has become widespread in research and industry. This surge of interest has resulted in a large and fragmented research field that faces technical challenges alongside fundamental debates, and it has left practitioners without clear guidance on how to effectively employ existing methods. Aiming to increase coherence and facilitate future work, in this review we provide a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-dimensional embeddings of high-dimensional data de Bodt, Cyril Diaz-Papkovich, Alex Bleher, Michael Bunte, Kerstin Coupette, Corinna Damrich, Sebastian Sanmartin, Enrique Fita Hamprecht, Fred A. Horvát, Emőke-Ágnes Kohli, Dhruv Krishnaswamy, Smita Lee, John A. Lelieveldt, Boudewijn P. F. McInnes, Leland Nabney, Ian T. Noichl, Maximilian Poličar, Pavlin G. Rieck, Bastian Wolf, Guy Mishne, Gal Kobak, Dmitry Machine Learning Large collections of high-dimensional data have become nearly ubiquitous across many academic fields and application domains, ranging from biology to the humanities. Since working directly with high-dimensional data poses challenges, the demand for algorithms that create low-dimensional representations, or embeddings, for data visualization, exploration, and analysis is now greater than ever. In recent years, numerous embedding algorithms have been developed, and their usage has become widespread in research and industry. This surge of interest has resulted in a large and fragmented research field that faces technical challenges alongside fundamental debates, and it has left practitioners without clear guidance on how to effectively employ existing methods. Aiming to increase coherence and facilitate future work, in this review we provide a detailed and critical overview of recent developments, derive a list of best practices for creating and using low-dimensional embeddings, evaluate popular approaches on a variety of datasets, and discuss the remaining challenges and open problems in the field. |
| title | Low-dimensional embeddings of high-dimensional data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.15929 |