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dc.contributor.authorVaiter, Samuel
HAL ID: 1995
ORCID: 0000-0002-4077-708X
dc.contributor.authorDeledalle, Charles-Alban
dc.contributor.authorPeyré, Gabriel
HAL ID: 1211
dc.contributor.authorFadili, Jalal
HAL ID: 15510
dc.contributor.authorDossal, Charles
dc.date.accessioned2012-05-16T15:08:52Z
dc.date.available2012-05-16T15:08:52Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/9250
dc.language.isoenen
dc.subjectgroup lasso
dc.subjectunbiased risk estimation
dc.subjectblock regularization
dc.subjectlocal variation
dc.subjectdegrees of freedom
dc.subjectsparsity
dc.subject.ddc621.3en
dc.titleThe Degrees of Freedom of the Group Lasso
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherInstitut de Mathématiques de Bordeaux (IMB) http://www.math.u-bordeaux.fr/IMB/ CNRS : UMR5251 – Université Sciences et Technologies - Bordeaux I – Université Victor Segalen - Bordeaux II;France
dc.contributor.editoruniversityotherGroupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC) http://www.greyc.unicaen.fr/ CNRS : UMR6072 – Université de Caen – Ecole Nationale Supérieure d'Ingénieurs de Caen;France
dc.description.abstractenThis paper studies the sensitivity to the observations of the block/group Lasso solution to an overdetermined linear regression model. Such a regularization is known to promote sparsity patterns structured as nonoverlapping groups of coefficients. Our main contribution provides a local parameterization of the solution with respect to the observations. As a byproduct, we give an unbiased estimate of the degrees of freedom of the group Lasso. Among other applications of such results, one can choose in a principled and objective way the regularization parameter of the Lasso through model selection criteria.
dc.publisher.cityParisen
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-00695292
dc.description.sponsorshipprivateouien
dc.subject.ddclabelTraitement du signalen
dc.relation.conftitleInternational Conference on Machine Learning Workshop (ICML), 2012
dc.relation.confcityEdinburgh
dc.relation.confcountryUNITED KINGDOM
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2016-10-13T09:23:07Z


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