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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/156105

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Title: Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing
Author: Belda, Jordi Vergara Domínguez, Luís Safont Armero, Gonzalo Salazar Afanador, Addisson
UPV Unit: 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
Issued date:
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 ...[+]
Subjects: Partial correlation , Independent component analysis , Graph signal processing
Copyrigths: Reconocimiento (by)
Source:
Entropy. (issn: 1099-4300 )
DOI: 10.3390/e21010022
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/e21010022
Project ID:
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/
Thanks:
This research was funded by Spanish Administration and European Union under grants TEC2014-58438-R and TEC2017-84743-P.
Type: Artículo

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