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 f unction (PDF) estimation. The two-stage process consists of spatial whiteni ng and a series of Givens rotations and produces a cost function consisting only of marginal entropies, This formulation avoids the problems of PDF in accuracy due to truncation of series expansion and the estimation of joint PDFs in high-dimensional spaces given the typical paucity of data, Simulati ons illustrate the superior efficiency, in terms of data length, of the pro posed method compared to fast independent component analysis (FastICA), Com on's minimum mutual information, and Bell and Sejnowski's Infomax.