Fast approximation of nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied. We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that speed of the algorithm is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

[1]  Christian Jutten,et al.  Quasi-nonparametric blind inversion of Wiener systems , 2001, IEEE Trans. Signal Process..

[2]  Christian Jutten,et al.  Entropy Optimization - Application to Blind Source Separation , 1997, ICANN.

[3]  T. Sejnowski,et al.  Separation of post-nonlinear mixtures using ACE and temporal decorrelation , 2001 .

[4]  Christian Jutten,et al.  What should we say about the kurtosis? , 1999, IEEE Signal Processing Letters.

[5]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[6]  Christian Jutten,et al.  Source separation in post-nonlinear mixtures , 1999, IEEE Trans. Signal Process..

[7]  Andrzej Cichocki,et al.  Information-theoretic approach to blind separation of sources in non-linear mixture , 1998, Signal Process..

[8]  Lucas C. Parra,et al.  Statistical Independence and Novelty Detection with Information Preserving Nonlinear Maps , 1996, Neural Computation.

[9]  Te-Won Lee,et al.  Blind source separation of nonlinear mixing models , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.