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dc.contributor.authorPilanci, M.en_US
dc.contributor.authorArıkan, Orhanen_US
dc.coverage.spatialPrague, Czech Republicen_US
dc.date.accessioned2016-02-08T12:18:58Z
dc.date.available2016-02-08T12:18:58Z
dc.date.issued2011en_US
dc.identifier.issn1520-6149
dc.identifier.urihttp://hdl.handle.net/11693/28375
dc.descriptionDate of Conference: 22-27 May 2011en_US
dc.description.abstractWe show that the exact recovery of sparse perturbations on the coefficient matrix in overdetermined Least Squares problems is possible for a large class of perturbation structures. The well established theory of Compressed Sensing enables us to prove that if the perturbation structure is sufficiently incoherent, then exact or stable recovery can be achieved using linear programming. We derive sufficiency conditions for both exact and stable recovery using known results of ℓ 0/ℓ 1 equivalence. However the problem turns out to be more complicated than the usual setting used in various sparse reconstruction problems. We propose and solve an optimization criterion and its convex relaxation to recover the perturbation and the solution to the Least Squares problem simultaneously. Then we demonstrate with numerical examples that the proposed method is able to recover the perturbation and the unknown exactly with high probability. The performance of the proposed technique is compared in blind identification of sparse multipath channels. © 2011 IEEE.en_US
dc.language.isoEnglishen_US
dc.source.title2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICASSP.2011.5947207en_US
dc.subjectCompressed Sensingen_US
dc.subjectMatrix Identificationen_US
dc.subjectSparse Multipath Channelsen_US
dc.subjectStructured Perturbationsen_US
dc.subjectStructured Total Least Squaresen_US
dc.subjectCompressed sensingen_US
dc.subjectMatrix identificationen_US
dc.subjectSparse multi-path channelen_US
dc.subjectStructured perturbationsen_US
dc.subjectStructured total least squaresen_US
dc.subjectCommunication channels (information theory)en_US
dc.subjectLeast squares approximationsen_US
dc.subjectMultipath propagationen_US
dc.subjectNumerical methodsen_US
dc.subjectOptimizationen_US
dc.subjectRelaxation processesen_US
dc.subjectSignal reconstructionen_US
dc.subjectSpeech communicationen_US
dc.subjectRecoveryen_US
dc.titleRecovery of sparse perturbations in Least Squares problemsen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.citation.spage3912en_US
dc.citation.epage3915en_US
dc.identifier.doi10.1109/ICASSP.2011.5947207en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorArıkan, Orhan


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