A unified framework for nonconvex nonsmooth sparse and low-rank decomposition by majorization-minimization algorithm (Q2095019)

scientific article; zbMATH DE number 7613840

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English	A unified framework for nonconvex nonsmooth sparse and low-rank decomposition by majorization-minimization algorithm	scientific article; zbMATH DE number 7613840

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title

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

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Journal of the Franklin Institute

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publication date

9 November 2022

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zbMATH Keywords

low-rank decomposition

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majorization-minimization algorithm

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alternating direction method of multipliers

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RASL

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Publication

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full work available at URL

https://doi.org/10.1016/j.jfranklin.2022.09.002

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cites work

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Background Subtraction Based on Low-Rank and Structured Sparse Decomposition

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Rank-Sparsity Incoherence for Matrix Decomposition

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Estimating differential latent variable graphical models with applications to brain connectivity

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Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm

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Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition

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Nonconvex Regularized Robust PCA Using the Proximal Block Coordinate Descent Algorithm

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Alternating Direction Method of Multipliers for a Class of Nonconvex and Nonsmooth Problems with Applications to Background/Foreground Extraction

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Generalized singular value thresholding operator based nonconvex low-rank and sparse decomposition for moving object detection

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Multiple-prespecified-dictionary sparse representation for compressive sensing image reconstruction with nonconvex regularization

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Nonconvex nonsmooth low-rank minimization for generalized image compressed sensing via group sparse representation

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Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

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Recovering low-rank and sparse matrix based on the truncated nuclear norm

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Weighted nuclear norm minimization and its applications to low level vision

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Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Norm Minimization for Image Denoising and Background Subtraction

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Stable recovery of low-rank matrix via nonconvex Schatten $p$-minimization

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Accelerated Alternating Projections for Robust Principal Component Analysis

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Best Pair Formulation & Accelerated Scheme for Non-Convex Principal Component Pursuit

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A generalized robust minimization framework for low-rank matrix recovery

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Reduced rank regression via adaptive nuclear norm penalization

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Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities

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Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning

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Decomposition method with a variable parameter for a class of monotone variational inequality problems

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Understanding Analysis

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Q4067293

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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties

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Identifiers

zbMATH Open document ID

1505.65187

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Mathematics Subject Classification ID

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10.1016/J.JFRANKLIN.2022.09.002

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Mathematics(1 entry)

mardi Publication:2095019