Robust techniques for independent component analysis (ICA) with noisy data
A Cichocki , S.C. Douglas , S. Amari
AbstractIn 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.
|Journal series||Neurocomputing, ISSN 0925-2312|
|Keywords in English||Bias removal, blind source separation, Independent component analysis (ICA), Maximum likelihood, natural gradient, Noise cancellation|
|Publication indicators||: 2006 = 0.86 (2) - 2007=0.914 (5)|
|Citation count*||76 (2014-02-08)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.