Separation of two independent sources by the information-theoretic approach with cubic nonlinearity
- 22 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4, 2239-2244
- https://doi.org/10.1109/icnn.1997.614377
Abstract
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.Keywords
This publication has 1 reference indexed in Scilit:
- An Information-Maximization Approach to Blind Separation and Blind DeconvolutionNeural Computation, 1995