From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices
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Publication:5265193
DOI10.1007/978-3-319-10605-2_2zbMath1376.94003arXiv1407.1120OpenAlexW1862697533MaRDI QIDQ5265193
Mathieu Salzmann, Mehrtash Harandi, Richard I. Hartley
Publication date: 22 July 2015
Published in: Computer Vision – ECCV 2014 (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1407.1120
Riemannian geometryGrassmann manifolddimensionality reductionvisual recognitionSymmetric Positive Definite (SPD) manifold
Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Classical differential geometry (53A99)
Related Items (7)
Dimension reduction and construction of feature space for image pattern recognition ⋮ Optimization Problems Associated with Manifold-Valued Curves with Applications in Computer Vision ⋮ Geometry-aware principal component analysis for symmetric positive definite matrices ⋮ Unsupervised manifold learning with polynomial mapping on symmetric positive definite matrices ⋮ Spatiotemporal analysis using Riemannian composition of diffusion operators ⋮ Geometry-based symbolic approximation for fast sequence matching on manifolds ⋮ Beyond covariance: SICE and kernel based visual feature representation
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