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Nonlinear estimators from ICA mixture models

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Nonlinear estimators from ICA mixture models

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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


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