Using the Penalized Mutual Information Criterion in the Multivariate Edgeworth-Expanded Gaussian Mixture Density for Blind Separation of Convolutive Post-Nonlinear Mixtures
Abstract:This paper proposes the blind separation of convolutive post-nonlinear (CPNL) mixtures based on the minimization of the penalized mutual information criterion. The proposed algorithm is based on the estimation score function difference (SFD) and the Newton optimization. Compared with the blind source separation of a linear mixture, the separation performance of a nonlinear mixture is strongly related to the accuracy of the score function estimation. Under this framework, the multivariate Edgeworth-expanded Gau… Show more
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.