New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> Minimization

New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> Minimization

In this paper, we consider using the weighted &#x2113;<sub>2,1</sub> minimization to reconstruct X from Y = AX + Z. This method has been applied to recover multichannel signal in resent years since it exploits both the interchannel correlation and multisource prior. We show improved...

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Bibliographic Details
Main Authors: Huanmin Ge, Run Cao
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Multiple measurement vector
restricted isometry property
the weighted ℓ₁,₂ minimization
Online Access:https://ieeexplore.ieee.org/document/8891695/
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spelling doaj-72a25521a8824f8aa8d6008cb23606052021-03-30T00:56:00ZengIEEEIEEE Access2169-35362019-01-01716715716717110.1109/ACCESS.2019.29515738891695New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> MinimizationHuanmin Ge0https://orcid.org/0000-0003-1132-373XRun Cao1https://orcid.org/0000-0002-0222-7896School of Sports Engineering, Beijing Sport University, Beijing, ChinaSchool of Sports Engineering, Beijing Sport University, Beijing, ChinaIn this paper, we consider using the weighted &#x2113;<sub>2,1</sub> minimization to reconstruct X from Y = AX + Z. This method has been applied to recover multichannel signal in resent years since it exploits both the interchannel correlation and multisource prior. We show improved sufficient conditions based on the restricted isometry property (RIP) for the exact and stable recovery of X via the weighted &#x2113;<sub>2,1</sub> minimization. Moreover, a sufficient condition based on the high order RIP is obtained to guarantee the recovery of X via the standard mixed-norm &#x2113;<sub>2,1</sub> minimization.https://ieeexplore.ieee.org/document/8891695/Multiple measurement vectorrestricted isometry propertythe weighted ℓ₁,₂ minimization
collection DOAJ
language English
format Article
sources DOAJ
author Huanmin Ge
Run Cao
spellingShingle Huanmin Ge
Run Cao
New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> Minimization
IEEE Access
Multiple measurement vector
restricted isometry property
the weighted ℓ₁,₂ minimization
author_facet Huanmin Ge
Run Cao
author_sort Huanmin Ge
title New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> Minimization
title_short New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> Minimization
title_full New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> Minimization
title_fullStr New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> Minimization
title_full_unstemmed New Bounds Based on RIP for the Sparse Matrix Recovery via the Weighted <inline-formula> <tex-math notation="LaTeX">$\ell_{2,1}$ </tex-math></inline-formula> Minimization
title_sort new bounds based on rip for the sparse matrix recovery via the weighted <inline-formula> <tex-math notation="latex">$\ell_{2,1}$ </tex-math></inline-formula> minimization
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description In this paper, we consider using the weighted &#x2113;<sub>2,1</sub> minimization to reconstruct X from Y = AX + Z. This method has been applied to recover multichannel signal in resent years since it exploits both the interchannel correlation and multisource prior. We show improved sufficient conditions based on the restricted isometry property (RIP) for the exact and stable recovery of X via the weighted &#x2113;<sub>2,1</sub> minimization. Moreover, a sufficient condition based on the high order RIP is obtained to guarantee the recovery of X via the standard mixed-norm &#x2113;<sub>2,1</sub> minimization.
topic Multiple measurement vector
restricted isometry property
the weighted ℓ₁,₂ minimization
url https://ieeexplore.ieee.org/document/8891695/
work_keys_str_mv AT huanminge newboundsbasedonripforthesparsematrixrecoveryviatheweightedinlineformulatexmathnotationlatexell21texmathinlineformulaminimization
AT runcao newboundsbasedonripforthesparsematrixrecoveryviatheweightedinlineformulatexmathnotationlatexell21texmathinlineformulaminimization
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