Blind source separation using Renyi´s mutual information

A blind source separation algorithm is proposed that is based on minimizing Renyi´s mutual information by means of nonparametric probability density function (PDF) estimation. The two-stage process consists of spatial whitening and a series of Givens rotations and produces a cost function consisting only of marginal entropies. This formulation avoids the problems of PDF inaccuracy due to truncation of series expansion and the estimation of joint PDFs in high-dimensional spaces given the typical paucity of data. Simulations illustrate the superior efficiency, in terms of data length, of the proposed method compared to fast independent component analysis (FastICA), Comon´s (1994) minimum mutual information, and Bell and Sejnowski´s (1995) Infomax.