Sparse signal recovery via ECME thresholding pursuits
type: Item
instance of: scholarly article
title: Sparse signal recovery via ECME thresholding pursuits
zbMATH Open document ID: 1264.94068
DOI: 10.1155/2012/478931
author: Heping Song
author: Guo-Li Wang
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: The emerging theory of compressive sensing (CS) provides a new sparse signal processing paradigm for reconstructing sparse signals from the undersampled linear measurements. Recently, numerous algorithms have been developed to solve convex optimization problems for CS sparse signal recovery. However, in some certain circumstances, greedy algorithms exhibit superior performance than convex methods. This paper is a followup to the recent paper of Wang and Yin (2010), who refine BP reconstructions via iterative support detection (ISD). The heuristic idea of ISD was applied to greedy algorithms. We developed two approaches for accelerating the ECME iteration. The described algorithms, named ECME thresholding pursuits (EMTP), introduced two greedy strategies that each iteration detects a support set I by thresholding the result of the ECME iteration and estimates the reconstructed signal by solving a truncated least-squares problem on the support set I. Two effective support detection strategies are devised for the sparse signals with components having a fast decaying distribution of nonzero components. The experimental studies are presented to demonstrate that EMTP offers an appealing alternative to state-of-the-art algorithms for sparse signal recovery.
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 6173315
Wikidata QID: Q58911752
describes a project that uses: SparseLab
describes a project that uses: PDCO
describes a project that uses: CoSaMP
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/478931
OpenAlex ID: W1980548182
cites work: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
cites work: Extensions of compressed sensing
cites work: Atomic decomposition by basis pursuit
cites work: From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
cites work: Sparse Approximate Solutions to Linear Systems
cites work: Q4864293
cites work: Least angle regression. (With discussion)
cites work: 10.1162/15324430152748236
cites work: Sparse Bayesian Learning for Basis Selection
cites work: Bayesian Compressive Sensing
cites work: Bayesian Compressive Sensing Using Laplace Priors
cites work: Matching pursuits with time-frequency dictionaries
cites work: Greed is Good: Algorithmic Results for Sparse Approximation
cites work: Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
cites work: Subspace Pursuit for Compressive Sensing Signal Reconstruction
cites work: CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
cites work: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
cites work: Iterative hard thresholding for compressed sensing
cites work: Variance-Component Based Sparse Signal Reconstruction and Model Selection
cites work: Sparse Signal Reconstruction via ECME Hard Thresholding
cites work: Sparse Signal Reconstruction via Iterative Support Detection
cites work: Gradient Pursuits
description: scientific article; zbMATH DE number 6173315