Exterior penalty function method based ICA algorithm for hybrid sources using GKNN estimation

Authors:
Fasong Wang;Hongwei Li;Rui Li
Affiliations:
School of Mathematics and Physics, China University of Geosciences, Wuhan, P.R. China;School of Mathematics and Physics, China University of Geosciences, Wuhan, P.R. China;School of Mathematics and Physics, Henan University of Technology, Zhengzhou, P.R. China
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

Novel Independent Component analysis(ICA) algorithm for hybrid sources separation based on constrained optimization—exterior penalty function method is proposed. The proposed exterior penalty ICA algorithm is under the framework of constrained ICA(cICA) method to solve the constrained optimization problem by using the exterior penalty function method. In order to choose nonlinear functions as the probability density function(PDF) estimation of the source signals, generalized k-nearest neighbor(GKNN) PDF estimation is proposed which can separate the hybrid mixtures of source signals using only a flexible model and more important it is completely blind to the sources. The proposed EX-cICA algorithm provides the way to wider applications of ICA methods to real world signal processing. Simulations confirm the effectiveness of the proposed algorithm.