Independent component analysis via optimum combining of kurtosis and skewness-based criteria

This paper introduces blind separation methods that are based on minimization of mutual information. Direct minimization of mutual information leads to estimating functions that change on every iteration of separating algorithm unlike in the maximum likelihood approach employing fixed non-linearity. We propose objective functions for source separation that are comprised of a symmetric and an asymmetric part. This allows for separating signals that may have skewed distributions. The optimal weighting between the symmetric and the asymmetric part is determined from the data based on an efficacy measure. The performance of the proposed objective functions is studied in cases where some source signals may be asymmetrically distributed. The capability of adapting to different type of source distributions is demonstrated in simulations.