Sparse Recovery With Block Multiple Measurement Vectors Algorithm Article Swipe

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YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1109/access.2019.2891568

This paper investigates the performance of the block multiple measurement vectors (BMMV) algorithm in reconstructing block joint sparse matrices. We prove that if 41) obeys block restricted isometry property with 8 K+1 <; Nf +1 , then BMMV perfectly reconstructs any block K -joint sparse matrix X from observations Y = (DX in K iterations. We also show that BMMV may not reconstruct block K -joint sparse matrices in K iterations under the condition K+1 > , 1 That is to say, the condition 8K +l <; , 1 is v1C+1 vIC+1 optimal for the BMMV algorithm.

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Restricted isometry property Block (permutation group theory) Algorithm Sparse matrix Property (philosophy) Joint (building) Isometry (Riemannian geometry) Matrix (chemical analysis) Computer science Mathematics Combinatorics Compressed sensing Physics Gaussian Pure mathematics Materials science Engineering Composite material Quantum mechanics Epistemology Architectural engineering Philosophy

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2019.2891568
PDF: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08614434.pdf
OA Status: gold
Cited By: 8
References: 31
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DOI: https://doi.org/10.1109/access.2019.2891568

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Title: Sparse Recovery With Block Multiple Measurement Vectors Algorithm

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Language: en

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Publication year: 2019

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Publication date: 2019-01-01

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Authors: Yanli Shi, Libo Wang, Rong Luo

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Landing page: https://doi.org/10.1109/access.2019.2891568

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Concepts: Restricted isometry property, Block (permutation group theory), Algorithm, Sparse matrix, Property (philosophy), Joint (building), Isometry (Riemannian geometry), Matrix (chemical analysis), Computer science, Mathematics, Combinatorics, Compressed sensing, Physics, Gaussian, Pure mathematics, Materials science, Engineering, Composite material, Quantum mechanics, Epistemology, Architectural engineering, Philosophy

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Cited by: 8

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Citations by year (recent): 2024: 2, 2023: 2, 2022: 1, 2021: 2, 2020: 1

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References (count): 31

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Related works (count): 10

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