Unsupervised manifold learning with polynomial mapping on symmetric positive definite matrices

DOI10.1016/j.ins.2022.07.077OpenAlexW4285808535WikidataQ114167302 ScholiaQ114167302MaRDI QIDQ6122194

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Publication date: 27 March 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2022.07.077

zbMATH Keywords

polynomial mappingmanifold learninggeodesic distancesymmetric positive-definite

Mathematics Subject Classification ID

Computer science (68-XX) Linear and multilinear algebra; matrix theory (15-XX)

Cites Work

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• Geometry of matrix decompositions seen through optimal transport and information geometry
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• Diffusion maps
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• 10.1162/153244304322972667
• Manifold Kernel Sparse Representation of Symmetric Positive-Definite Matrices and Its Applications
• Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• Manifold-valued image processing with SPD matrices
• From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices
• Target Detection Within Nonhomogeneous Clutter Via Total Bregman Divergence-Based Matrix Information Geometry Detectors

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