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Entropy 2019, 21(1), 22; https://doi.org/10.3390/e21010022

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

Institute of Telecommunications and Multimedia Applications, Universitat Politècnica de València, 46022 València, Spain
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Received: 23 November 2018 / Revised: 23 December 2018 / Accepted: 24 December 2018 / Published: 29 December 2018
(This article belongs to the Special Issue Information Theory Applications in Signal Processing)
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Abstract

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. View Full-Text
Keywords: partial correlation; independent component analysis; graph signal processing partial correlation; independent component analysis; graph signal processing
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Belda, J.; Vergara, L.; Safont, G.; Salazar, A. Computing the Partial Correlation of ICA Models for Non-Gaussian Graph Signal Processing. Entropy 2019, 21, 22.

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