Complex ICA using generalized uncorrelating transform

Abstract

An extension of the whitening transformation for complex random vectors, called the generalized uncorrelating transformation (GUT), is introduced. GUT is a generalization of the strong-uncorrelating transform [J. Eriksson, V. Koivunen, Complex-valued ICA using 2nd-order statistics, in: Proceedings of the IEEE Workshop on Machine Learning for Signal Processing (MLSP’04), Sao Luis, Brazil, 2004] based upon generalized estimators of the covariance and pseudo-covariance matrix, called the scatter matrix and spatial pseudo-scatter matrix, respectively. Depending on the selected scatter and spatial pseudo-scatter matrix, GUT estimators can have largely different statistical properties. Special emphasis is put on robust GUT estimators. We show that GUT is a separating matrix estimator for complex-valued independent component analysis (ICA) when at most one source random variable possess circularly symmetric distribution and sources do not have identical distribution. In the context of ICA, our approach is computationally attractive as it is based on straightforward matrix computations. Simulations and examples are used to confirm reliable performance of our method.