Blind source separation using least-squares type adaptive algorithms
Juha Karhunen, Petteri Pajunen · 2002
Adaptive least-squares type algorithms are introduced for blind source separation. They are based on minimizing a criterion used in context with nonlinear PCA (principal component analysis) networks. The new algorithms converge clearly faster and provide more accurate results than typical current adaptive blind separation algorithms based on instantaneous gradients. They are also applicable to the difficult case of nonstationary mixtures. The proposed algorithms have a close relationship to a nonlinear extension of Oja's (see Computational Intelligence-a Dynamic System Perspective, p.83-97, IEEE Press, 1995) PCA learning rule. A batch algorithm based on the same criterion is also presented.