Positive Definite Matrices: Data Representation and Applications to Computer Vision

DOI10.1007/978-3-319-45026-1_4zbMath1355.65031OpenAlexW2528144786MaRDI QIDQ2954277

Suvrit Sra, Anoop Cherian

Publication date: 12 January 2017

Published in: Algorithmic Advances in Riemannian Geometry and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-45026-1_4

zbMATH Keywords

algorithmcomputer visionpositive definite matricesmachine learningdictionary learningsparse coding

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Positive matrices and their generalizations; cones of matrices (15B48)

Related Items (4)

An Empirical Bayes Approach to Shrinkage Estimation on the Manifold of Symmetric Positive-Definite Matrices ⋮ Unsupervised assignment flow: label learning on feature manifolds by spatially regularized geometric assignment ⋮ 7 Manifold interpolation ⋮ Assignment flow for order-constrained OCT segmentation

Uses Software

• SPG
• L-BFGS
• Manopt
• CUReT
• FERET

Cites Work

• On the limited memory BFGS method for large scale optimization
• Trust-region methods on Riemannian manifolds
• A Riemannian framework for tensor computing
• Manopt, a Matlab toolbox for optimization on manifolds
• Description of States in Quantum Mechanics by Density Matrix and Operator Techniques
• Nonmonotone Spectral Projected Gradient Methods on Convex Sets
• Algorithm 813

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