Learned parametric mixture based ICA algorithm

The learned parametric mixture method is presented for a canonical cost fun ction based ICA model on linear mixture, with several new findings. First, its adaptive algorithm is further refined into a simple concise form. Secon d, the separation ability of this method is shown to be qualitatively super ior to its original model with prefixed nonlinearity. Third, a heuristic wa y is suggested for selecting the number of densities in a Beamed parametric mixture. Finally, experiments have been conducted to show the success of t his method on the sources that can either be sub-Gaussian or super-Gaussian , as well as a combination of both the types. (C) 1998 Elsevier Science B.V . All rights reserved.