A novel robust principal component analysis method for image and video processing.

DOI10.1007/s10492-016-0128-8zbMath1389.62096OpenAlexW2302215559MaRDI QIDQ265164

Guoqiang Huan, Ying Li, Zhan-jie Song

Publication date: 1 April 2016

Published in: Applications of Mathematics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10338.dmlcz/144844

zbMATH Keywords

matrix factorizationMarkov random fieldsrobust principal component analysiscontiguity priorsparse Bayesian learning

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Probability in computer science (algorithm analysis, random structures, phase transitions, etc.) (68Q87)

Related Items (1)

Decomposition into low-rank plus additive matrices for background/foreground separation: a review for a comparative evaluation with a large-scale dataset

Uses Software

• Regularization tools
• UTV
• softImpute
• PRMLT
• RASL

Cites Work

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• A framework for robust subspace learning
• Principal component analysis.
• Exact matrix completion via convex optimization
• Robust principal component analysis?
• Markov Random Field Modeling in Image Analysis
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Sparse Bayesian Methods for Low-Rank Matrix Estimation
• Bayesian Robust Principal Component Analysis
• Robust PCA via Outlier Pursuit
• The Power of Convex Relaxation: Near-Optimal Matrix Completion

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