Blind separation of nonlinear mixtures by variational Bayesian learning

Blind separation of sources from nonlinear mixtures is a challenging and often ill-posed problem. We present three methods for solving this problem: an improved nonlinear factor analysis (NFA) method using a multilayer perceptron (MLP) network to model the nonlinearity, a hierarchical NFA (HNFA) method suitable for larger problems and a post-nonlinear NFA (PNFA) method for more restricted post-nonlinear mixtures. The methods are based on variational Bayesian learning, which provides the needed regularisation and allows for easy handling of missing data. While the basic methods are incapable of recovering the correct rotation of the source space, they can discover the underlying nonlinear manifold and allow reconstruction of the original sources using standard linear independent component analysis (ICA) techniques.