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dc.contributor.authorSuliman, Mohamed Abdalla Elhag
dc.contributor.authorBallal, Tarig
dc.contributor.authorKammoun, Abla
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.date.accessioned2017-01-02T09:28:31Z
dc.date.available2017-01-02T09:28:31Z
dc.date.issued2016-12-19
dc.identifier.citationSuliman M, Ballal T, Kammoun A, Al-Naffouri TY (2016) Penalized linear regression for discrete ill-posed problems: A hybrid least-squares and mean-squared error approach. 2016 24th European Signal Processing Conference (EUSIPCO). Available: http://dx.doi.org/10.1109/EUSIPCO.2016.7760279.
dc.identifier.doi10.1109/EUSIPCO.2016.7760279
dc.identifier.urihttp://hdl.handle.net/10754/622448
dc.description.abstractThis paper proposes a new approach to find the regularization parameter for linear least-squares discrete ill-posed problems. In the proposed approach, an artificial perturbation matrix with a bounded norm is forced into the discrete ill-posed model matrix. This perturbation is introduced to enhance the singular-value (SV) structure of the matrix and hence to provide a better solution. The proposed approach is derived to select the regularization parameter in a way that minimizes the mean-squared error (MSE) of the estimator. Numerical results demonstrate that the proposed approach outperforms a set of benchmark methods in most cases when applied to different scenarios of discrete ill-posed problems. Jointly, the proposed approach enjoys the lowest run-time and offers the highest level of robustness amongst all the tested methods.
dc.description.sponsorshipThis work was supported by the King Abdulaziz City of Science and Technology (KACST) under Grant AT-34-345.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7760279/
dc.subjectregularization
dc.subjectlinear estimation
dc.subjectlinear least-squares
dc.subjectill-posed problem
dc.titlePenalized linear regression for discrete ill-posed problems: A hybrid least-squares and mean-squared error approach
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journal2016 24th European Signal Processing Conference (EUSIPCO)
dc.conference.date2016-08-28 to 2016-09-02
dc.conference.name24th European Signal Processing Conference, EUSIPCO 2016
dc.conference.locationBudapest, HUN
kaust.personSuliman, Mohamed Abdalla Elhag
kaust.personBallal, Tarig
kaust.personKammoun, Abla
kaust.personAl-Naffouri, Tareq Y.


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