Results 31 to 40 of about 981,446 (269)
Orthogonal Matching Pursuit with Replacement
CoRR, 2011 In this paper, we consider the problem of compressed sensing where the goal is to recover almost all the sparse vectors using a small number of fixed linear measurements. For this problem, we propose a novel partial hard-thresholding operator that leads to a general family of iterative algorithms.
Prateek Jain 0002 +2 more
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Efficiency of Orthogonal Matching Pursuit for Group Sparse Recovery
Axioms, 2023 We propose the Group Orthogonal Matching Pursuit (GOMP) algorithm to recover group sparse signals from noisy measurements. Under the group restricted isometry property (GRIP), we prove the instance optimality of the GOMP algorithm for any decomposable ...
Chunfang Shao +3 more
doaj +1 more source
Iterative thresholding for sparse approximations [PDF]
, 2008 Sparse signal expansions represent or approximate a signal using a small number of elements from a large collection of elementary waveforms. Finding the optimal sparse expansion is known to be NP hard in general and non-optimal strategies such as ...
Blumensath, T. +3 more
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A reduced-complexity compressed sensing channel estimation for underwater acoustic channel
Dianxin kexue, 2021 Aiming at the sparse characteristics of underwater acoustic channels for shallow seas, a reduced-complexity look-ahead backtracking orthogonal matching pursuit (RC-LABOMP) channel estimation algorithm was proposed.Firstly, two types of support sets of ...
Xuan YU, Xuan GENG
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Xibei Gongye Daxue Xuebao, 2020 Efficient localization of multiple targets is one of the basic technical problems in wireless sensor networks (WSN). The traditional sparse representation method based on greedy class is not efficient in multi-target positioning.
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Backward-Optimized Orthogonal Matching Pursuit Approach [PDF]
IEEE Signal Processing Letters, 2004 A recursive approach for shrinking coefficients of an atomic decomposition is proposed. The corresponding algorithm evolves so as to provide at each iteration 1) the orthogonal projection of a signal onto a reduced subspace and 2) the index of the coefficient to be disregarded in order to construct a coarser approximation minimizing the norm of the ...
Miroslav Andrle +2 more
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Quantum matching pursuit algorithms [PDF]
, 2022 LAUREA MAGISTRALEIl machine learning quantistico è una disciplina emergente che combina i benefici del calcolo quantistico con tecniche di machine learning.
VANERIO, STEFANO
core
Tuning Free Orthogonal Matching Pursuit
CoRR, 2017 Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depends on its stopping criteria (SC). SC for OMP discussed in literature typically assumes knowledge of either the sparsity of the signal to be estimated $k_0$ or noise variance ...
Sreejith Kallummil, Sheetal Kalyani
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A CONSTRAINED MATCHING PURSUIT APPROACH TO AUDIO DECLIPPING [PDF]
, 2011 © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new ...
Elad, M +6 more
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An improved reconstruction algorithm based on compressed sensing for power quality analysis
Cogent Engineering, 2016 The application and analysis of compressive sensing theory in power quality has been received more and more attention. Reconstruction algorithm is one of the most important contents of the compressive sensing theory, and as one of the reconstruction ...
Quandang Ma +3 more
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