A generalized nonconvex algorithm framework for low-rank and sparse matrix decomposition

The low-rank and sparse matrix decomposition problem is a hot and challenging problem in computer science. In this paper, we consider it as a nonconvex relaxation optimization problem by using a family of nonconvex functions to approximate the rank function and the -norm in low-rank and sparse matri...

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Vydané v:Applied intelligence (Dordrecht, Netherlands) Ročník 55; číslo 16; s. 1085
Hlavní autori: Cui, Angang, Zhang, Lijun, He, Haizhen, Xue, Shengli
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.11.2025
Springer Nature B.V
Predmet:
Adaptive algorithms
Algorithms
Approximation
Artificial Intelligence
Computer Science
Decomposition
Machines
Manufacturing
Mechanical Engineering
Optimization
Parameters
Processes
Regularization
Sparse matrices
Sparsity
Nonconvex algorithm framework
Nonconvex function
regularization Reneralized low-rank and sparse matrix decomposition problem
Low-rank and sparse matrix decomposition
ISSN:0924-669X, 1573-7497
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Shrnutí:The low-rank and sparse matrix decomposition problem is a hot and challenging problem in computer science. In this paper, we consider it as a nonconvex relaxation optimization problem by using a family of nonconvex functions to approximate the rank function and the -norm in low-rank and sparse matrix decomposition problem, namely, generalized low-rank and sparse matrix decomposition problem. The essence of this paper is to develop an adaptive algorithm framework with parameters updating for the nonconvex relaxation problem. Firstly, we prove the equivalence between the generalized low-rank and sparse matrix decomposition problem and the regularization generalized low-rank and sparse matrix decomposition problem. This means that the optimal solution of generalized low-rank and sparse matrix decomposition problem can be exactly obtained by solving its regularization minimization problem. Secondly, we present a tractable nonconvex algorithm framework to solve the regularization generalized low-rank and sparse matrix decomposition problem. The convergence analysis of the algorithm framework is provided. More importantly, we also define a very powerful parameter-setting strategy to adapt the optimal parameters in iteration of the proposed algorithm framework. Finally, we test the proposed algorithms on some random low-rank and sparse matrix decomposition problems, and the numerical results verified the effectiveness of the proposed algorithms. In addition, we also extend the proposed algorithms to the image denoising and background modeling from surveillance video.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-025-06971-8