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dc.contributor.authorBallal, Tarig
dc.contributor.authorSuliman, Mohamed Abdalla Elhag
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.date.accessioned2017-11-06T07:09:04Z
dc.date.available2017-11-06T07:09:04Z
dc.date.issued2017-10-18
dc.identifier.citationBallal T, Suliman MA, Al-Naffouri TY (2017) Bounded Perturbation Regularization for Linear Least Squares Estimation. IEEE Access: 1–1. Available: http://dx.doi.org/10.1109/ACCESS.2017.2759201.
dc.identifier.issn2169-3536
dc.identifier.doi10.1109/ACCESS.2017.2759201
dc.identifier.urihttp://hdl.handle.net/10754/626111
dc.description.abstractThis paper addresses the problem of selecting the regularization parameter for linear least-squares estimation. We propose a new technique called bounded perturbation regularization (BPR). In the proposed BPR method, a perturbation with a bounded norm is allowed into the linear transformation matrix to improve the singular-value structure. Following this, the problem is formulated as a min-max optimization problem. Next, the min-max problem is converted to an equivalent minimization problem to estimate the unknown vector quantity. The solution of the minimization problem is shown to converge to that of the ℓ2 -regularized least squares problem, with the unknown regularizer related to the norm bound of the introduced perturbation through a nonlinear constraint. A procedure is proposed that combines the constraint equation with the mean squared error (MSE) criterion to develop an approximately optimal regularization parameter selection algorithm. Both direct and indirect applications of the proposed method are considered. Comparisons with different Tikhonov regularization parameter selection methods, as well as with other relevant methods, are carried out. Numerical results demonstrate that the proposed method provides significant improvement over state-of-the-art methods.
dc.description.sponsorshipThis work was supported by the KAUST-KFUPM joint research initiative and the KAUST CRG3 funding.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8070950/
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
dc.subjectBusiness process re-engineering
dc.subjectCovariance matrices
dc.subjectEstimation
dc.subjectleast squares
dc.subjectLinear estimation
dc.subjectMatrix converters
dc.subjectmean squared error
dc.subjectMinimization
dc.subjectOptimization
dc.subjectPeriodic structures
dc.subjectTikhonov regularization
dc.titleBounded Perturbation Regularization for Linear Least Squares Estimation
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Access
dc.eprint.versionPost-print
kaust.personBallal, Tarig
kaust.personSuliman, Mohamed Abdalla Elhag
kaust.personAl-Naffouri, Tareq Y.
refterms.dateFOA2018-06-13T13:46:52Z
dc.date.published-online2017-10-18
dc.date.published-print2017


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