A unified framework for nonconvex nonsmooth sparse and low-rank decomposition by majorization-minimization algorithm

DOI10.1016/j.jfranklin.2022.09.002zbMath1505.65187OpenAlexW4295277374MaRDI QIDQ2095019

Qian-Zhen Zheng, Ping-feng Xu

Publication date: 9 November 2022

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2022.09.002

zbMATH Keywords

alternating direction method of multipliersmajorization-minimization algorithmlow-rank decomposition

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical optimization and variational techniques (65K10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Uses Software

• RASL

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Reduced rank regression via adaptive nuclear norm penalization
• Latent variable graphical model selection via convex optimization
• Nonconvex nonsmooth low-rank minimization for generalized image compressed sensing via group sparse representation
• Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities
• A generalized robust minimization framework for low-rank matrix recovery
• Multiple-prespecified-dictionary sparse representation for compressive sensing image reconstruction with nonconvex regularization
• Weighted nuclear norm minimization and its applications to low level vision
• Recovering low-rank and sparse matrix based on the truncated nuclear norm
• Generalized singular value thresholding operator based nonconvex low-rank and sparse decomposition for moving object detection
• Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning
• Stable recovery of low-rank matrix via nonconvex Schatten \(p\)-minimization
• Handbook of Robust Low-Rank and Sparse Matrix Decomposition
• Robust principal component analysis?
• Rank-Sparsity Incoherence for Matrix Decomposition
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition
• Background Subtraction Based on Low-Rank and Structured Sparse Decomposition
• Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm
• Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Norm Minimization for Image Denoising and Background Subtraction
• Accelerated Alternating Projections for Robust Principal Component Analysis
• Estimating differential latent variable graphical models with applications to brain connectivity
• Best Pair Formulation & Accelerated Scheme for Non-Convex Principal Component Pursuit
• Nonconvex Regularized Robust PCA Using the Proximal Block Coordinate Descent Algorithm
• Understanding Analysis
• Alternating Direction Method of Multipliers for a Class of Nonconvex and Nonsmooth Problems with Applications to Background/Foreground Extraction
• Decomposition method with a variable parameter for a class of monotone variational inequality problems

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