Robust techniques for independent component analysis (ICA) with noisy data

Abstract

In this contribution, we propose approaches to independent component analysis (ICA) when the measured signals are contaminated by additive noise. We extend existing adaptive algorithms with equivariant properties in order to considerably reduce the bias in the demixing matrix caused by measurement noise. Moreover, we describe a novel recurrent dynamic neural network for simultaneous estimation of the unknown mixing matrix, blind source separation, and reduction of noise in the extracted output signals. We discuss the optimal choice of nonlinear activation functions for various noise distributions assuming a generalized Gaussian-distributed noise model. Computer simulations of a selected approach are provided that confirm its usefulness and excellent performance.