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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
National Academy of Sciences
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8307776/ https://www.ncbi.nlm.nih.gov/pubmed/34272279 http://dx.doi.org/10.1073/pnas.2100473118 |