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Dimensionality Reduction of SPD Data Based on Riemannian Manifold Tangent Spaces and Isometry
Symmetric positive definite (SPD) data have become a hot topic in machine learning. Instead of a linear Euclidean space, SPD data generally lie on a nonlinear Riemannian manifold. To get over the problems caused by the high data dimensionality, dimensionality reduction (DR) is a key subject for SPD...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471569/ https://www.ncbi.nlm.nih.gov/pubmed/34573742 http://dx.doi.org/10.3390/e23091117 |