A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866917375586074624 |
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| author | Bi, Zelong de Micheaux, Pierre Lafaye |
| author_facet | Bi, Zelong de Micheaux, Pierre Lafaye |
| contents | The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging its structure, yet existing work on dimension estimation is fragmented and lacks systematic evaluation. This article provides a comprehensive survey for both researchers and practitioners. We review often-overlooked theoretical foundations and present eight representative estimators. Through controlled experiments, we analyze how individual factors, such as noise, curvature, and sample size, affect performance. We also compare the estimators on diverse synthetic and real-world datasets, introducing a principled approach to dataset-specific hyperparameter tuning. Our results offer practical guidance for estimator selection and yield insights that will inform future estimator design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15517 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis Bi, Zelong de Micheaux, Pierre Lafaye Machine Learning Applications The manifold hypothesis suggests that high-dimensional data often lie on or near a low-dimensional manifold. Estimating the dimension of this manifold is essential for leveraging its structure, yet existing work on dimension estimation is fragmented and lacks systematic evaluation. This article provides a comprehensive survey for both researchers and practitioners. We review often-overlooked theoretical foundations and present eight representative estimators. Through controlled experiments, we analyze how individual factors, such as noise, curvature, and sample size, affect performance. We also compare the estimators on diverse synthetic and real-world datasets, introducing a principled approach to dataset-specific hyperparameter tuning. Our results offer practical guidance for estimator selection and yield insights that will inform future estimator design. |
| title | A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis |
| topic | Machine Learning Applications |
| url | https://arxiv.org/abs/2509.15517 |