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hal.structure.identifierDepartment of Electrical and Computer Engineering [Durham] [ECE]
dc.contributor.authorHunt, Xin Jiang*
hal.structure.identifierLaboratoire Jean Alexandre Dieudonné [JAD]
dc.contributor.authorReynaud-Bouret, Patricia
HAL ID: 8239
*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorRivoirard, Vincent*
hal.structure.identifierMathématiques et Informatique Appliquées [MIA-Paris]
dc.contributor.authorSansonnet, Laure
HAL ID: 15296
*
hal.structure.identifierDepartment of Electrical and Computer Engineering [Durham] [ECE]
dc.contributor.authorWillett, Rebecca*
dc.date.accessioned2019-09-04T12:36:47Z
dc.date.available2019-09-04T12:36:47Z
dc.date.issued2019
dc.identifier.issn0018-9448
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19685
dc.language.isoenen
dc.subjectWeighted LASSOen
dc.subjectPoisson noiseen
dc.subjectcompressed sensingen
dc.subjectgenetic motifsen
dc.subjectphoton-limited imagingen
dc.subject.ddc519en
dc.titleA Data-Dependent Weighted LASSO Under Poisson Noiseen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenSparse linear inverse problems appear in a variety of settings, but often the noise contaminating observations cannot accurately be described as bounded by or arising from a Gaussian distribution. Poisson observations in particular are a characteristic feature of several real-world applications. Previous work on sparse Poisson inverse problems encountered several limiting technical hurdles. This paper describes a novel alternative analysis approach for sparse Poisson inverse problems that 1) sidesteps the technical challenges present in previous work, 2) admits estimators that can readily be computed using off-the-shelf LASSO algorithms, and 3) hints at a general framework for broad classes of noise in sparse linear inverse problems. At the heart of this new approach lies a weighted LASSO estimator for which data-dependent weights are based on Poisson concentration inequalities. Unlike previous analyses of the weighted LASSO, the proposed analysis depends on conditions which can be checked or shown to hold in general settings with high probability.en
dc.relation.isversionofjnlnameIEEE Transactions on Information Theory
dc.relation.isversionofjnlvol65en
dc.relation.isversionofjnlissue3en
dc.relation.isversionofjnldate2019-03
dc.relation.isversionofjnlpages1589-1613en
dc.relation.isversionofdoi10.1109/TIT.2018.2869578en
dc.relation.isversionofjnlpublisherIEEE - Institute of Electrical and Electronics Engineersen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2019-09-04T12:30:34Z
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