Elements of information theory
Elements of information theory
Information-theoretic approach to blind separation of sources in non-linear mixture
Signal Processing - Special issue on neural networks
Entropy Optimization - Application to Blind Source Separation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
Quasi-nonparametric blind inversion of Wiener systems
IEEE Transactions on Signal Processing
Maximum likelihood linear programming data fusion for speaker recognition
Speech Communication
An evolutionary approach for blind inversion of wiener systems
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Post nonlinear independent subspace analysis
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Separation theorem for independent subspace analysis and its consequences
Pattern Recognition
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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.