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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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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

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Título: Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing
Autor: Belda, Jordi Vergara Domínguez, Luís Safont Armero, Gonzalo Salazar Afanador, Addisson
Entidad UPV: Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia
Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Partial correlation , Independent component analysis , Graph signal processing
Derechos de uso: Reconocimiento (by)
Fuente:
Entropy. (issn: 1099-4300 )
DOI: 10.3390/e21010022
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/e21010022
Código del Proyecto:
info:eu-repo/grantAgreement/MINECO//TEC2014-58438-R/ES/PROCESADO DE SEÑAL SOBRE GRAFOS PARA SISTEMAS CLASIFICADORES: APLICACION EN SALUD, ENERGIA Y SEGURIDAD/
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/
Agradecimientos:
This research was funded by Spanish Administration and European Union under grants TEC2014-58438-R and TEC2017-84743-P.
Tipo: Artículo

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