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dc.contributor.authorVilla, Silvia
dc.contributor.authorRosasco, Lorenzo Andrea
dc.contributor.authorMosci, Sofia
dc.contributor.authorVerri, Alessandro
dc.date.accessioned2016-06-22T21:00:44Z
dc.date.available2016-06-22T21:00:44Z
dc.date.issued2013-12
dc.date.submitted2012-10
dc.identifier.issn0926-6003
dc.identifier.issn1573-2894
dc.identifier.urihttp://hdl.handle.net/1721.1/103284
dc.description.abstractWe consider a regularized least squares problem, with regularization by structured sparsity-inducing norms, which extend the usual ℓ[subscript 1] and the group lasso penalty, by allowing the subsets to overlap. Such regularizations lead to nonsmooth problems that are difficult to optimize, and we propose in this paper a suitable version of an accelerated proximal method to solve them. We prove convergence of a nested procedure, obtained composing an accelerated proximal method with an inner algorithm for computing the proximity operator. By exploiting the geometrical properties of the penalty, we devise a new active set strategy, thanks to which the inner iteration is relatively fast, thus guaranteeing good computational performances of the overall algorithm. Our approach allows to deal with high dimensional problems without pre-processing for dimensionality reduction, leading to better computational and prediction performances with respect to the state-of-the art methods, as shown empirically both on toy and real data.en_US
dc.publisherSpringer USen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10589-013-9628-6en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer USen_US
dc.titleProximal methods for the latent group lasso penaltyen_US
dc.typeArticleen_US
dc.identifier.citationVilla, Silvia, Lorenzo Rosasco, Sofia Mosci, and Alessandro Verri. “Proximal Methods for the Latent Group Lasso Penalty.” Comput Optim Appl 58, no. 2 (December 21, 2013): 381–407.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMcGovern Institute for Brain Research at MITen_US
dc.contributor.mitauthorRosasco, Lorenzo Andreaen_US
dc.relation.journalComputational Optimization and Applicationsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2016-05-23T12:15:40Z
dc.language.rfc3066en
dc.rights.holderSpringer Science+Business Media New York
dspace.orderedauthorsVilla, Silvia; Rosasco, Lorenzo; Mosci, Sofia; Verri, Alessandroen_US
dspace.embargo.termsNen
dc.identifier.orcidhttps://orcid.org/0000-0001-6376-4786
mit.licensePUBLISHER_POLICYen_US


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