Distribution agnostic structured sparsity recovery algorithms

Handle URI:
http://hdl.handle.net/10754/564716
Title:
Distribution agnostic structured sparsity recovery algorithms
Authors:
Al-Naffouri, Tareq Y.; Masood, Mudassir ( 0000-0003-0462-7874 )
Abstract:
We present an algorithm and its variants for sparse signal recovery from a small number of its measurements in a distribution agnostic manner. The proposed algorithm finds Bayesian estimate of a sparse signal to be recovered and at the same time is indifferent to the actual distribution of its non-zero elements. Termed Support Agnostic Bayesian Matching Pursuit (SABMP), the algorithm also has the capability of refining the estimates of signal and required parameters in the absence of the exact parameter values. The inherent feature of the algorithm of being agnostic to the distribution of the data grants it the flexibility to adapt itself to several related problems. Specifically, we present two important extensions to this algorithm. One extension handles the problem of recovering sparse signals having block structures while the other handles multiple measurement vectors to jointly estimate the related unknown signals. We conduct extensive experiments to show that SABMP and its variants have superior performance to most of the state-of-the-art algorithms and that too at low-computational expense. © 2013 IEEE.
KAUST Department:
Electrical Engineering Program
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)
Conference/Event name:
2013 8th International Workshop on Systems, Signal Processing and Their Applications, WoSSPA 2013
Issue Date:
May-2013
DOI:
10.1109/WoSSPA.2013.6602377
Type:
Conference Paper
ISBN:
9781467355407
Appears in Collections:
Conference Papers; Electrical Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.contributor.authorMasood, Mudassiren
dc.date.accessioned2015-08-04T07:13:29Zen
dc.date.available2015-08-04T07:13:29Zen
dc.date.issued2013-05en
dc.identifier.isbn9781467355407en
dc.identifier.doi10.1109/WoSSPA.2013.6602377en
dc.identifier.urihttp://hdl.handle.net/10754/564716en
dc.description.abstractWe present an algorithm and its variants for sparse signal recovery from a small number of its measurements in a distribution agnostic manner. The proposed algorithm finds Bayesian estimate of a sparse signal to be recovered and at the same time is indifferent to the actual distribution of its non-zero elements. Termed Support Agnostic Bayesian Matching Pursuit (SABMP), the algorithm also has the capability of refining the estimates of signal and required parameters in the absence of the exact parameter values. The inherent feature of the algorithm of being agnostic to the distribution of the data grants it the flexibility to adapt itself to several related problems. Specifically, we present two important extensions to this algorithm. One extension handles the problem of recovering sparse signals having block structures while the other handles multiple measurement vectors to jointly estimate the related unknown signals. We conduct extensive experiments to show that SABMP and its variants have superior performance to most of the state-of-the-art algorithms and that too at low-computational expense. © 2013 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleDistribution agnostic structured sparsity recovery algorithmsen
dc.typeConference Paperen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journal2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)en
dc.conference.date12 May 2013 through 15 May 2013en
dc.conference.name2013 8th International Workshop on Systems, Signal Processing and Their Applications, WoSSPA 2013en
dc.conference.locationAlgiersen
kaust.authorAl-Naffouri, Tareq Y.en
kaust.authorMasood, Mudassiren
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