Fast Blind Separation of Long Mixture Recordings Using Multivariate Polynomial Identification

This correspondence presents new approaches for optimizing kurtosis-based separation criteria in the case of long mixture recordings. Our methods are based on a multivariate polynomial identification step that avoids the computation of signal statistics at each step of the commonly used fixed-point optimization algorithms. As compared to the well-known FastICA algorithm and to our recent DFICA algorithm intended for blind partial separation of nonstationary sources, our new methods are very computationally efficient for long recordings of a moderate number of mixed sources. They are therefore especially suited to blind image separation, because of the high number of pixels in light sensors. Our algorithms also avoid the computation and storage of the sphered observation vector, thus saving memory space.