Sparse recovery using expanders via hard thresholding algorithm

Expanders play an important role in combinatorial compressed sensing. Via expanders measurements, we propose the expander normalized heavy ball hard thresholding algorithm (ENHB-HT) based on expander iterative hard thresholding (E-IHT) algorithm. We provide convergence analysis of ENHB-HT, and it tu...

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Bibliographic Details
Published in:Signal processing Vol. 227; p. 109715
Main Authors: Wen, Kun-Kai, He, Jia-Xin, Li, Peng
Format: Journal Article
Language:English
Published: Elsevier B.V 01.02.2025
Subjects:
Combinatorial compressed sensing
Hard thresholding
Heavy ball method
Lossless expanders
Sparse signal recovery
94A15
Heavy ball method
Hard thresholding
90C26
Lossless expanders
Sparse signal recovery
94A12
Combinatorial compressed sensing
ISSN:0165-1684
Online Access:Get full text
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Summary:Expanders play an important role in combinatorial compressed sensing. Via expanders measurements, we propose the expander normalized heavy ball hard thresholding algorithm (ENHB-HT) based on expander iterative hard thresholding (E-IHT) algorithm. We provide convergence analysis of ENHB-HT, and it turns out that ENHB-HT can recover an s-sparse signal if the measurement matrix A∈{0,1}m×n satisfies some mild conditions. Numerical experiments are simulated to support our two main theorems which describe the convergence rate and the accuracy of the proposed algorithm. Simulations are also performed to compare the performance of ENHB-HT and several existing algorithms under different types of noise, the empirical results demonstrate that our algorithm outperform a few existing ones in the presence of outliers. •Propose the expander normalized heavy ball hard thresholding algorithm.•Convergence rate and accuracy of proposed algorithm are provided.•Our method achieves good numerical performance, especially in the presence of outliers.
ISSN:0165-1684
DOI:10.1016/j.sigpro.2024.109715