Adaptive ICA Algorithm Based on Asymmetric Generalized Gaussian Density Model

Abstract

A novel Independent Component Analysis(ICA) algorithm is achieved, which enable to separate mixtures of symmetric and asymmetric sources with self adaptive nonlinear score functions. It is derived by using the parameterized asymmetric generalized Gaussian density (AGGD) model. Compared with conventional ICA algorithm, the proposed AGGD-ICA method can separate a wide range of signals including skewed sources. Simulations confirm the effectiveness and performance of the approach.