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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Detalles Bibliográficos
Autores principales: Gao, Wenxu, Ma, Zhengming, Gan, Weichao, Liu, Shuyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
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

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471569/
https://www.ncbi.nlm.nih.gov/pubmed/34573742
http://dx.doi.org/10.3390/e23091117

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