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...
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| Автори: | , , , |
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| Формат: | Artigo |
| Мова: | Inglês |
| Опубліковано: |
MDPI AG
2021-08-01
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| Серія: | Entropy |
| Предмети: | |
| Онлайн доступ: | https://www.mdpi.com/1099-4300/23/9/1117 |
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