The degrees of freedom of partly smooth regularizers
| hal.structure.identifier | CEntre de REcherches en MAthématiques de la DEcision [CEREMADE] | |
| dc.contributor.author | VAITER, Samuel | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | DELEDALLE, Charles-Alban | |
| hal.structure.identifier | Equipe Image - Laboratoire GREYC - UMR6072 | |
| dc.contributor.author | FADILI, Jalal M. | |
| hal.structure.identifier | CEntre de REcherches en MAthématiques de la DEcision [CEREMADE] | |
| dc.contributor.author | PEYRÉ, Gabriel | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | DOSSAL, Charles | |
| dc.date.created | 2015-10-23 | |
| dc.date.issued | 2017-08 | |
| dc.identifier.issn | 0020-3157 | |
| dc.description.abstractEn | We study regularized regression problems where the regularizer is a proper, lower-semicontinuous, convex and partly smooth function relative to a Riemannian submanifold. This encompasses several popular examples including the Lasso, the group Lasso, the max and nuclear norms, as well as their composition with linear operators (e.g., total variation or fused Lasso). Our main sensitivity analysis result shows that the predictor moves locally stably along the same active submanifold as the observations undergo small perturbations. This plays a pivotal role in getting a closed-form expression for the divergence of the predictor w.r.t. observations. We also show that, for many regularizers, including polyhedral ones or the analysis group Lasso, this divergence formula holds Lebesgue a.e. When the perturbation is random (with an appropriate continuous distribution), this allows us to derive an unbiased estimator of the degrees of freedom and the prediction risk. Our results unify and go beyond those already known in the literature. | |
| dc.language.iso | en | |
| dc.publisher | Springer Verlag | |
| dc.subject.en | Manifold | |
| dc.subject.en | O-minimal structures | |
| dc.subject.en | Model selection | |
| dc.subject.en | Sparsity | |
| dc.subject.en | Degrees of freedom | |
| dc.subject.en | Semi-algebraic sets | |
| dc.subject.en | Total variation | |
| dc.subject.en | Group Lasso | |
| dc.subject.en | Partial smoothness | |
| dc.title.en | The degrees of freedom of partly smooth regularizers | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1007/s10463-016-0563-z | |
| dc.subject.hal | Mathématiques [math]/Statistiques [math.ST] | |
| dc.subject.hal | Mathématiques [math]/Théorie de l'information et codage [math.IT] | |
| dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
| dc.subject.hal | Sciences de l'ingénieur [physics]/Traitement du signal et de l'image | |
| dc.subject.hal | Statistiques [stat]/Théorie [stat.TH] | |
| dc.subject.hal | Informatique [cs]/Théorie de l'information [cs.IT] | |
| dc.identifier.arxiv | 1404.5557 | |
| dc.description.sponsorshipEurope | Sparsity, Image and Geometry to Model Adaptively Visual Processings | |
| bordeaux.journal | Annals of the Institute of Statistical Mathematics | |
| bordeaux.page | 791 – 832 | |
| bordeaux.volume | 69 | |
| bordeaux.issue | 4 | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-00981634 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-00981634v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Annals%20of%20the%20Institute%20of%20Statistical%20Mathematics&rft.date=2017-08&rft.volume=69&rft.issue=4&rft.spage=791%20%E2%80%93%20832&rft.epage=791%20%E2%80%93%20832&rft.eissn=0020-3157&rft.issn=0020-3157&rft.au=VAITER,%20Samuel&DELEDALLE,%20Charles-Alban&FADILI,%20Jalal%20M.&PEYR%C3%89,%20Gabriel&DOSSAL,%20Charles&rft.genre=article |
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