Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms

In this paper, we propose a modified version of the hard thresholding pursuit algorithm, called modified hard thresholding pursuit (MHTP), using a convex combination of the current and previous points. The convergence analysis, finite termination properties, and stability of the MHTP are established...

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Main Authors: Li-Ping Geng, Jin-Chuan Zhou, Zhong-Feng Sun, Jing-Yong Tang
Format: Article
Language:English
Published: Wiley 2023-01-01
Series:IET Signal Processing
Online Access:http://dx.doi.org/10.1049/2023/9937696
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author Li-Ping Geng
Jin-Chuan Zhou
Zhong-Feng Sun
Jing-Yong Tang
author_facet Li-Ping Geng
Jin-Chuan Zhou
Zhong-Feng Sun
Jing-Yong Tang
author_sort Li-Ping Geng
collection DOAJ
description In this paper, we propose a modified version of the hard thresholding pursuit algorithm, called modified hard thresholding pursuit (MHTP), using a convex combination of the current and previous points. The convergence analysis, finite termination properties, and stability of the MHTP are established under the restricted isometry property of the measurement matrix. Simulations are performed in noiseless and noisy environments using synthetic data, in which the successful frequencies, average runtime, and phase transition of the MHTP are considered. Standard test images are also used to test the reconstruction capability of the MHTP in terms of the peak signal-to-noise ratio. Numerical results indicate that the MHTP is competitive with several mainstream thresholding and greedy algorithms, such as hard thresholding pursuit, compressive sampling matching pursuit, subspace pursuit, generalized orthogonal matching pursuit, and Newton-step-based hard thresholding pursuit, in terms of recovery capability and runtime.
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institution Kabale University
issn 1751-9683
language English
publishDate 2023-01-01
publisher Wiley
record_format Article
series IET Signal Processing
spelling doaj-art-5327348cbf4c448fab2069539c41f6502025-02-03T06:47:46ZengWileyIET Signal Processing1751-96832023-01-01202310.1049/2023/9937696Recovery of Sparse Signals via Modified Hard Thresholding Pursuit AlgorithmsLi-Ping Geng0Jin-Chuan Zhou1Zhong-Feng Sun2Jing-Yong Tang3School of Mathematics and StatisticsSchool of Mathematics and StatisticsSchool of Mathematics and StatisticsSchool of Mathematics and StatisticsIn this paper, we propose a modified version of the hard thresholding pursuit algorithm, called modified hard thresholding pursuit (MHTP), using a convex combination of the current and previous points. The convergence analysis, finite termination properties, and stability of the MHTP are established under the restricted isometry property of the measurement matrix. Simulations are performed in noiseless and noisy environments using synthetic data, in which the successful frequencies, average runtime, and phase transition of the MHTP are considered. Standard test images are also used to test the reconstruction capability of the MHTP in terms of the peak signal-to-noise ratio. Numerical results indicate that the MHTP is competitive with several mainstream thresholding and greedy algorithms, such as hard thresholding pursuit, compressive sampling matching pursuit, subspace pursuit, generalized orthogonal matching pursuit, and Newton-step-based hard thresholding pursuit, in terms of recovery capability and runtime.http://dx.doi.org/10.1049/2023/9937696
spellingShingle Li-Ping Geng
Jin-Chuan Zhou
Zhong-Feng Sun
Jing-Yong Tang
Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms
IET Signal Processing
title Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms
title_full Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms
title_fullStr Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms
title_full_unstemmed Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms
title_short Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms
title_sort recovery of sparse signals via modified hard thresholding pursuit algorithms
url http://dx.doi.org/10.1049/2023/9937696
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AT zhongfengsun recoveryofsparsesignalsviamodifiedhardthresholdingpursuitalgorithms
AT jingyongtang recoveryofsparsesignalsviamodifiedhardthresholdingpursuitalgorithms