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dc.contributor.author | Safont Armero, Gonzalo | es_ES |
dc.contributor.author | Salazar Afanador, Addisson | es_ES |
dc.contributor.author | Vergara Domínguez, Luís | es_ES |
dc.contributor.author | RODRIGUEZ MARTINEZ, ALBERTO | es_ES |
dc.date.accessioned | 2020-12-18T04:31:46Z | |
dc.date.available | 2020-12-18T04:31:46Z | |
dc.date.issued | 2019-02 | es_ES |
dc.identifier.issn | 0165-1684 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/157360 | |
dc.description | "NOTICE: this is the author's version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, 155, (2019) https://doi.org/10.1016/j.sigpro.2018.10.003" | es_ES |
dc.description.abstract | [EN] Independent Component Analyzers Mixture Models (ICAMM) are versatile and general models for a large variety of probability density functions. In this paper we assume ICAMM to derive new MAP and LMSE estimators. The first one (MAP-ICAMM) is obtained by an iterative gradient algorithm, while the second (LMSE-ICAMM) admits a closed-form solution. Both estimators can be combined by using LMSE-ICAMM to initialize the iterative computation of MAP-ICAMM .The new estimators are applied to the reconstruction of missed channels in EEG multichannel analysis. The experiments demonstrate the superiority of the new estimators with respect to: Spherical Splines, Hermite, Partial Least Squares, Support Vector Regression, and Random Forest Regression. (C) 2018 Elsevier B.V. All rights reserved. | es_ES |
dc.description.sponsorship | This work was supported by Spanish Administration (Ministerio de Economia y Competitividad) and European Union (FEDER) under grant TEC2014-58438-R, and Generalitat Valenciana under grant PROMETEO II/2014/032. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Signal Processing | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | ICA | es_ES |
dc.subject | Nonlinear estimators | es_ES |
dc.subject | LMSE | es_ES |
dc.subject | MAP | es_ES |
dc.subject | EEG reconstruction | es_ES |
dc.subject | Non-Gaussian mixtures | es_ES |
dc.subject.classification | TEORIA DE LA SEÑAL Y COMUNICACIONES | es_ES |
dc.title | Nonlinear estimators from ICA mixture models | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.sigpro.2018.10.003 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TEC2014-58438-R/ES/PROCESADO DE SEÑAL SOBRE GRAFOS PARA SISTEMAS CLASIFICADORES: APLICACION EN SALUD, ENERGIA Y SEGURIDAD/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//PROMETEOII%2F2014%2F032/ES/TÉCNICAS AVANZADAS DE FUSIÓN EN TRATAMIENTO DE SEÑALES/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia | es_ES |
dc.description.bibliographicCitation | Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L.; Rodriguez Martinez, A. (2019). Nonlinear estimators from ICA mixture models. Signal Processing. 155:281-286. https://doi.org/10.1016/j.sigpro.2018.10.003 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.sigpro.2018.10.003 | es_ES |
dc.description.upvformatpinicio | 281 | es_ES |
dc.description.upvformatpfin | 286 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 155 | es_ES |
dc.relation.pasarela | S\408021 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |