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dc.contributor.authorWeruaga Prieto, Luis
dc.contributor.authorVía Rodríguez, Javier 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2017-01-05T18:12:56Z
dc.date.available2017-01-05T18:12:56Z
dc.date.issued2015-05
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.urihttp://hdl.handle.net/10902/9925
dc.description.abstractFitting a multivariate Gaussian mixture to data represents an attractive, as well as challenging problem, in especial when sparsity in the solution is demanded. Achieving this objective requires the concurrent update of all parameters (weight, centers, and precisions) of all multivariate Gaussian functions during the learning process. Such is the focus of this paper, which presents a novel method founded on the minimization of the error of the generalized logarithmic utility function (GLUF). This choice, which allows us to move smoothly from the mean square error (MSE) criterion to the one based on the logarithmic error, yields an optimization problem that resembles a locally convex problem and can be solved with a quasi-Newton method. The GLUF framework also facilitates the comparative study between both extremes, concluding that the classical MSE optimization is not the most adequate for the task. The performance of the proposed novel technique is demonstrated on simulated as well as realistic scenarios.es_ES
dc.format.extent11 p.es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineeerses_ES
dc.rights© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.es_ES
dc.sourceIEEE Transactions on Neural Networks and Learning Systems, 2015, 26(5), 1098 - 1108es_ES
dc.subject.otherGaussian function mixturees_ES
dc.subject.otherFunction approximationes_ES
dc.subject.otherRegressiones_ES
dc.subject.otherLogarithmic utility functiones_ES
dc.subject.otherSparsityes_ES
dc.titleSparse multivariate Gaussian mixture regressiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttps://doi.org/10.1109/TNNLS.2014.2334596es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1109/TNNLS.2014.2334596
dc.type.versionacceptedVersiones_ES


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