Dimensionality Reduction of SPD Data Based on Riemannian Manifold Tangent Spaces and Isometry

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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Bibliographic Details
Main Authors: Wenxu Gao, Zhengming Ma, Weichao Gan, Shuyu Liu
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Entropy
Subjects:
dimensionality reduction
tangent space
symmetric positive
definite matrices
isometry
Online Access:https://www.mdpi.com/1099-4300/23/9/1117

Internet

https://www.mdpi.com/1099-4300/23/9/1117

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