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hal.structure.identifierLaboratoire d'analyse des données et d'intelligence des systèmes [LADIS]
dc.contributor.authorIsaac, Yoann
hal.structure.identifierMensia Technologies [Rennes]
dc.contributor.authorBarthélemy, Quentin
hal.structure.identifierLaboratoire d'analyse des données et d'intelligence des systèmes [LADIS]
dc.contributor.authorGouy-Pailler, Cédric
HAL ID: 6827
ORCID: 0000-0003-1298-7845
hal.structure.identifierLaboratoire de Recherche en Informatique [LRI]
dc.contributor.authorSebag, Michèle
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorAtif, Jamal
HAL ID: 15689
dc.date.accessioned2017-01-27T16:11:25Z
dc.date.available2017-01-27T16:11:25Z
dc.date.issued2017
dc.identifier.issn0165-1684
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/16216
dc.language.isoenen
dc.subjectMachine learningen
dc.subjectStructured sparsityen
dc.subjectOvercomplete representationsen
dc.subjectAnalysis prioren
dc.subjectSplit Bregmanen
dc.subjectFused-LASSOen
dc.subjectEEG denoisingen
dc.subject.ddc006.3en
dc.titleMulti-dimensional signal approximation with sparse structured priors using split Bregman iterationsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper addresses the structurally constrained sparse decomposition of multi-dimensional signals onto overcomplete families of vectors, called dictionaries. The contribution of the paper is threefold. Firstly, a generic spatio-temporal regularization term is designed and used together with the standard ℓ1ℓ1 regularization term to enforce a sparse decomposition preserving the spatio-temporal structure of the signal. Secondly, an optimization algorithm based on the split Bregman approach is proposed to handle the associated optimization problem, and its convergence is analyzed. Our well-founded approach yields same accuracy as the other algorithms at the state of the art, with significant gains in terms of convergence speed. Thirdly, the empirical validation of the approach on artificial and real-world problems demonstrates the generality and effectiveness of the method. On artificial problems, the proposed regularization subsumes the Total Variation minimization and recovers the expected decomposition. On the real-world problem of electro-encephalography brainwave decomposition, the approach outperforms similar approaches in terms of P300 evoked potentials detection, using structured spatial priors to guide the decomposition.en
dc.relation.isversionofjnlnameSignal Processing
dc.relation.isversionofjnlvol130en
dc.relation.isversionofjnldate2017-01
dc.relation.isversionofjnlpages389-402en
dc.relation.isversionofdoi10.1016/j.sigpro.2016.07.013en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelIntelligence artificielleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2017-01-27T15:53:14Z
hal.identifierhal-01448305*
hal.version1*
hal.author.functionaut
hal.author.functionaut
hal.author.functionaut
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hal.author.functionaut


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