Independent component analysis by the information-theoretic approach with mixture of densities

A novel implementation technique of the information-theoretic approach to the independent component analysis problem is devised. This new algorithm uses the mixtures of densities as flexible models for the density functions of the source signals and they are tuned adaptively to approximate the marginal densities of the recovered signals. We suggest that the adaptive, flexible models for the density functions have the advantage that they can adapt source signals with any distribution, while pre-selected, fixed models, which appear as fixed nonlinearities in the algorithm, may only work on source signals with a particular class of distribution. Experiments have demonstrated the above assertions.