Bayesian versus constrained structure approaches for source separation in post-nonlinear mixtures

The work presents experimental comparison of two approaches introduced for solving the nonlinear blind source separation (BSS) problem: the Bayesian methods developed at Helsinki University of Technology (HUT), and the BSS methods introduced for post-nonlinear (PNL mixtures at Institut National Polytechnique de Grenoble (INPG). The comparison is performed on artificial test problems containing PNL mixtures. Both the standard case when the number of sources is equal to the number of observations and the case of overdetermined mixtures are considered. A new interesting result of the experiments is that globally invertible PNL mixtures, but with non-invertible component-wise nonlinearities, can be identified and sources can be separated, which shows the relevance of exploiting more observations than sources.