File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: New RIP Bounds for Recovery of Sparse Signals With Partial Support Information via Weighted ℓp-Minimization

TitleNew RIP Bounds for Recovery of Sparse Signals With Partial Support Information via Weighted ℓp-Minimization
Authors
KeywordsMinimization
Noise measurement
Tools
Two dimensional displays
Compressed sensing
Issue Date2020
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?puNumber=18
Citation
IEEE Transactions on Information Theory, 2020, v. 66 n. 6, p. 3914-3928 How to Cite?
AbstractIn this paper, we consider the recovery of k-sparse signals using the weighted ℓ p (0 <; p ≤ 1) minimization when some partial prior information on the support is available. First, we present a unified analysis of restricted isometry constant δ tk with d <; t ≤ 2d (d ) ≥1 is determined by the prior support information) for sparse signal recovery by the weighted ℓ p (0 <; p ≤ 1) minimization in both noiseless and noisy settings. This result fills a vacancy on δ tk with t <; 2, compared with previous works on δ (a+1)k (a > 1). Second, we provide a sufficient condition on δ tk with 1 <; t ≤ 2 for the recovery of sparse signals using the ℓ p (0 <; p ≤ 1) minimization, which extends the existing optimal result on δ 2k in the literature. Last, various numerical examples are presented to demonstrate the better performance of the weighted ℓ p (0 <; p ≤ 1) minimization is achieved when the accuracy of prior information on the support is at least 50%, compared with that of the ℓ p (0 <; p ≤1) minimization.
Persistent Identifierhttp://hdl.handle.net/10722/288100
ISSN
2019 Impact Factor: 3.036
2015 SCImago Journal Rankings: 1.433
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGe, H-
dc.contributor.authorChen, W-
dc.contributor.authorNg, MK-
dc.date.accessioned2020-10-05T12:07:52Z-
dc.date.available2020-10-05T12:07:52Z-
dc.date.issued2020-
dc.identifier.citationIEEE Transactions on Information Theory, 2020, v. 66 n. 6, p. 3914-3928-
dc.identifier.issn0018-9448-
dc.identifier.urihttp://hdl.handle.net/10722/288100-
dc.description.abstractIn this paper, we consider the recovery of k-sparse signals using the weighted ℓ p (0 <; p ≤ 1) minimization when some partial prior information on the support is available. First, we present a unified analysis of restricted isometry constant δ tk with d <; t ≤ 2d (d ) ≥1 is determined by the prior support information) for sparse signal recovery by the weighted ℓ p (0 <; p ≤ 1) minimization in both noiseless and noisy settings. This result fills a vacancy on δ tk with t <; 2, compared with previous works on δ (a+1)k (a > 1). Second, we provide a sufficient condition on δ tk with 1 <; t ≤ 2 for the recovery of sparse signals using the ℓ p (0 <; p ≤ 1) minimization, which extends the existing optimal result on δ 2k in the literature. Last, various numerical examples are presented to demonstrate the better performance of the weighted ℓ p (0 <; p ≤ 1) minimization is achieved when the accuracy of prior information on the support is at least 50%, compared with that of the ℓ p (0 <; p ≤1) minimization.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?puNumber=18-
dc.relation.ispartofIEEE Transactions on Information Theory-
dc.rightsIEEE Transactions on Information Theory. Copyright © IEEE.-
dc.rights©20xx 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 collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectMinimization-
dc.subjectNoise measurement-
dc.subjectTools-
dc.subjectTwo dimensional displays-
dc.subjectCompressed sensing-
dc.titleNew RIP Bounds for Recovery of Sparse Signals With Partial Support Information via Weighted ℓp-Minimization-
dc.typeArticle-
dc.identifier.emailNg, MK: michael.ng@hku.hk-
dc.identifier.authorityNg, MK=rp02578-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIT.2020.2966436-
dc.identifier.scopuseid_2-s2.0-85092508807-
dc.identifier.hkuros315738-
dc.identifier.volume66-
dc.identifier.issue6-
dc.identifier.spage3914-
dc.identifier.epage3928-
dc.identifier.isiWOS:000538158400041-
dc.publisher.placeUnited States-
dc.identifier.issnl0018-9448-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats