Blind source separation and tracking using nonlinear PCA criterion: a least-squares approach

In standard blind source separation, one tries to extract unknown source signals from their instantaneous linear mixtures by using minimum a priori information. We have recently shown that certain nonlinear PCA type neural learning rules can be successfully applied to this problem. In this paper, we introduce computationally efficient least-squares type algorithms for the basic blind source separation problem. The proposed algorithms can still be regarded neural, and they have a close relationship to our previous algorithms. The new algorithms converge faster and provide more accurate final results than our previous instantaneous stochastic gradient type learning algorithms. We also consider blind tracking of sources from nonstationary mixtures.