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Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry
Most high-dimensional datasets are thought to be inherently low-dimensional—that is, data points are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here, we study the viability of two approaches from differential geometry to estimate the Riemannian cur...
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| 出版年: | Proc Natl Acad Sci U S A |
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| 主要な著者: | , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
National Academy of Sciences
2021
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8307776/ https://ncbi.nlm.nih.gov/pubmed/34272279 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.2100473118 |
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