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Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing

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Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing

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dc.contributor.author Belda, Jordi es_ES
dc.contributor.author Vergara Domínguez, Luís es_ES
dc.contributor.author Safont Armero, Gonzalo es_ES
dc.contributor.author Salazar Afanador, Addisson es_ES
dc.date.accessioned 2020-12-01T04:32:21Z
dc.date.available 2020-12-01T04:32:21Z
dc.date.issued 2019-01 es_ES
dc.identifier.issn 1099-4300 es_ES
dc.identifier.uri http://hdl.handle.net/10251/156105
dc.description.abstract [EN] Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context. es_ES
dc.description.sponsorship This research was funded by Spanish Administration and European Union under grants TEC2014-58438-R and TEC2017-84743-P. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Entropy es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Partial correlation es_ES
dc.subject Independent component analysis es_ES
dc.subject Graph signal processing es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/e21010022 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/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2017-84743-P/ES/METODOS INFORMADOS PARA LA SINTESIS DE SEÑALES/ es_ES
dc.rights.accessRights Abierto 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.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Belda, J.; Vergara Domínguez, L.; Safont Armero, G.; Salazar Afanador, A. (2019). Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing. Entropy. 21(1):1-16. https://doi.org/10.3390/e21010022 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/e21010022 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 16 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 21 es_ES
dc.description.issue 1 es_ES
dc.relation.pasarela S\376290 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES
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