Blind Source Separation in Post Nonlinear Mixtures

This work implements alternative algorithms to that of Taleb and Jutten for blind source separation in post nonlinear mixtures. We use the same mutual information criterion as them, but we exploit its invariance with respect to translation to express its relative gradient in terms of the derivatives of the nonlinear transformations. Then we develop algorithms based on these derivatives. In a semi-parametric approach, the latter are parametrized by piecewise constant functions. All algorithms require only the estimation of the score functions of the reconstructed sources. A new method for score function estimation is presented for this purpose.