We identify a bilinear isometric mapping from high-dimensional SPD space to a low-dimensional SPD embedding such that the resulting representation maximizes ...
To this end, we model the bilinear isometric mapping to identify a low-dimensional embedding that maximizes the preservation of Riemannian geodesic distance. A ...
To this end, we model the bilinear isometric mapping to identify a low-dimensional embedding that maximizes the preservation of Riemannian geodesic distance. A ...
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Classification of symmetric positive definite matrices based on bilinear isometric Riemannian embedding. X Xie, ZL Yu, Z Gu, Y Li. Pattern Recognition 87, 94- ...
Dimensionality Reduction of SPD Data Based on Riemannian ... - NCBI
www.ncbi.nlm.nih.gov › PMC8471569
Aug 27, 2021 · Symmetric positive definite (SPD) data have become a hot topic in machine learning. Instead of a linear Euclidean space, SPD data generally lie ...
This paper introduces a new metric and mean on the set of positive semidefinite matrices of fixed-rank. The proposed metric is derived from a well-chosen ...
Classification of symmetric positive definite matrices based on bilinear isometric Riemannian embedding. X Xie, ZL Yu, Z Gu, Y Li. Pattern Recognition 87, 94- ...
Bilinear isometric mapping is proposed to extract embedding of Riemannian manifold. • The proposed method can maximize the preservation of Riemannian geodesic ...
Symmetric Positive Definite (SPD) matrices have be-come popular to encode image information. Accounting for the geometry of the Riemannian manifold of SPD ...
Classification of symmetric positive definite matrices based on bilinear isometric Riemannian embedding, PR(87), 2019, pp. 94-105. Elsevier DOI 1812