Multi-stage convex relaxation method for low-rank and sparse matrix separation problem

DOI10.1016/j.amc.2016.03.001zbMath1410.65133OpenAlexW2312387974MaRDI QIDQ1733456

Qin Zhang, Le Han

Publication date: 21 March 2019

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2016.03.001

zbMATH Keywords

nonconvexconvex relaxationmulti-stagelow-rank and sparse matrix separation

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50)

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

• LMaFit
• RASL

Cites Work

• Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
• TILT: transform invariant low-rank textures
• Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
• Robust principal component analysis?
• A Singular Value Thresholding Algorithm for Matrix Completion
• Rank-Sparsity Incoherence for Matrix Decomposition
• Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition
• Nonlocal Sparse and Low-Rank Regularization for Optical Flow Estimation
• Robust PCA via Outlier Pursuit
• Robust Matrix Decomposition With Sparse Corruptions
• Median filtering‐based methods for static background extraction from surveillance video
• Augmented Lagrangian alternating direction method for matrix separation based on low-rank factorization
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

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