Separation of two independent sources by the information-theoretic approach with cubic nonlinearity

We investigate the use of the simplest nonlinearity - cubic nonlinearity by the information-theoretic approach on two signals in the independent component analysis (ICA) problem. The mathematical analysis in this paper provides a global description of the cost function in the parameter space. It has also been proved that the general gradient algorithm can perform source separation on mixtures of two sources whose distributions are sub-Gaussian in average. Experiments that demonstrate the results are presented. This paper provides an interesting insight in the role of nonlinearity in adaptive ICA algorithm.