dc.contributor.author | Rosasco, Lorenzo Andrea | |
dc.contributor.author | Villa, Silvia | |
dc.contributor.author | Mosci, Sofia | |
dc.contributor.author | Santoro, Matteo | |
dc.contributor.author | Verri, Alessandro | |
dc.date.accessioned | 2013-10-18T13:09:05Z | |
dc.date.available | 2013-10-18T13:09:05Z | |
dc.date.issued | 2013-07 | |
dc.date.submitted | 2012-08 | |
dc.identifier.issn | 1532-4435 | |
dc.identifier.issn | 1533-7928 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/81424 | |
dc.description.abstract | In this work we are interested in the problems of supervised learning and variable selection when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to consider a sparse nonparametric model, hence avoiding linear or additive models. The key idea is to measure the importance of each variable in the model by making use of partial derivatives. Based on this intuition we propose a new notion of nonparametric sparsity and a corresponding least squares regularization scheme. Using concepts and results from the theory of reproducing kernel Hilbert spaces and proximal methods, we show that the proposed learning algorithm corresponds to a minimization problem which can be provably solved by an iterative procedure. The consistency properties of the obtained estimator are studied both in terms of prediction and selection performance. An extensive empirical analysis shows that the proposed method performs favorably with respect to the state-of-the-art methods. | en_US |
dc.description.sponsorship | United States. Defense Advanced Research Projects Agency. Information Processing Techniques Office | en_US |
dc.description.sponsorship | United States. Defense Advanced Research Projects Agency. System Science Division. Defense Sciences Office | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (Grant NSF-0640097) | en_US |
dc.description.sponsorship | National Science Foundation (U.S.) (Grant NSF-0827427) | en_US |
dc.description.sponsorship | Adobe Systems | en_US |
dc.description.sponsorship | Honda Research Institute USA, Inc. | en_US |
dc.description.sponsorship | Eugene McDermott Foundation | en_US |
dc.description.sponsorship | Sony Corporation | en_US |
dc.description.sponsorship | NEC | en_US |
dc.language.iso | en_US | |
dc.publisher | Association for Computing Machinery (ACM) | en_US |
dc.relation.isversionof | http://jmlr.org/papers/volume14/rosasco13a/rosasco13a.pdf | en_US |
dc.rights | Article 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.source | MIT Press | en_US |
dc.title | Nonparametric Sparsity and Regularization | en_US |
dc.type | Article | en_US |
dc.identifier.citation | Rosasco, Lorenzo et al. “Nonparametric Sparsity and Regularization.” Journal of Machine Learning Research 14 (2013): 1665–1714. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences | en_US |
dc.contributor.mitauthor | Rosasco, Lorenzo Andrea | en_US |
dc.relation.journal | Journal of Machine Learning Research | en_US |
dc.eprint.version | Final published version | en_US |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
dspace.orderedauthors | Rosasco, Lorenzo; Villa, Silvia; Mosci, Sofia; Santoro, Matteo; Verri, Alessandro | en_US |
dc.identifier.orcid | https://orcid.org/0000-0001-6376-4786 | |
mit.license | PUBLISHER_POLICY | en_US |
mit.metadata.status | Complete | |