Non-signal components minimization for sparse signal recovery

Sparse signal recovery is a challenging task. This paper focuses on the technique of non-signal components minimization for recovering the latent sparsest signal behind observations from two viewpoints: minimizing the residual subject to the sparsity level and minimizing the sparsity subject to the...

Full description

Saved in:
Bibliographic Details
Published in:Signal processing Vol. 226; p. 109617
Main Authors: Xiang, Min, Zhang, Zhenyue
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2025
Subjects:
Compressed sensing
Proximal gradient
Sparse signal recovery
Sparsity increment
Proximal gradient
Sparse signal recovery
Sparsity increment
Compressed sensing
ISSN:0165-1684
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Sparse signal recovery is a challenging task. This paper focuses on the technique of non-signal components minimization for recovering the latent sparsest signal behind observations from two viewpoints: minimizing the residual subject to the sparsity level and minimizing the sparsity subject to the residual constraint. A successive sparsity increment method is proposed to address these two closely related tasks. This method yields two algorithms: SINSM for residual minimization and SISP for sparsity minimization. Additionally, an improved accelerated proximal gradient algorithm is provided to solve each non-signal components minimization problem penalized by the residual. The convergence of the improved algorithm is guaranteed. Numerical experiments demonstrate the superior performance of SINSM and SISP on both synthetic and real-world data. •Theoretical equivalence of non-signal components minimization and signal pursuing.•Sparsity increment overcomes the weakness of frequently meeting local minima.•Improved accelerated PG solves the successive minimization problem and converges.•SINSM and SISP minimize residual or sparsity via incremental sparsity.•SINSM and SISP significantly outperform the compared sparse algorithms numerically.
ISSN:0165-1684
DOI:10.1016/j.sigpro.2024.109617