A NEW MODEL FOR SPARSE AND LOW-RANK MATRIX DECOMPOSITION

DOI10.11948/2017037zbMath1488.65103OpenAlexW2595682877MaRDI QIDQ5149591

Jianchao Bai, Zisheng Liu, Xuenian Liu, Guo Li, Ji-Cheng Li

Publication date: 11 February 2021

Published in: Journal of Applied Analysis & Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.11948/2017037

zbMATH Keywords

sparse matrixmatrix decompositionrobust principal component analysisnuclear normlow-rank matrix

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Computational methods for sparse matrices (65F50) Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Related Items (9)

General parameterized proximal point algorithm with applications in statistical learning ⋮ Lower bounds for the low-rank matrix approximation ⋮ A separable surrogate function method for sparse and low-rank matrices decomposition ⋮ A partially proximal S-ADMM for separable convex optimization with linear constraints ⋮ Generalized symmetric ADMM for separable convex optimization ⋮ A faster generalized ADMM-based algorithm using a sequential updating scheme with relaxed step sizes for multiple-block linearly constrained separable convex programming ⋮ A parameterized proximal point algorithm for separable convex optimization ⋮ A partial PPA block-wise ADMM for multi-block linearly constrained separable convex optimization ⋮ An inexact ADMM with proximal-indefinite term and larger stepsize

Uses Software

• PROPACK

Cites Work

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• On the rank minimization problem over a positive semidefinite linear matrix inequality
• Semidefinite Programming
• Real-Time Discriminative Background Subtraction

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