An ensemble learning approach to nonlinear dynamic blind source separation using state-space models

We propose a new method for learning a nonlinear dynamical state-space model in unsupervised manner. The proposed method can be viewed as a nonlinear dynamic generalization of standard linear blind source separation (BSS) or independent component analysis (ICA). Using ensemble learning, the method finds a nonlinear dynamical process which can explain the observations. The nonlinearities are modeled with multilayer perceptron networks. In ensemble learning, a simpler approximative distribution is fitted to the true posterior distribution by minimizing their Kullback-Leibler divergence. This also regularizes the studied highly ill-posed problem. In an experiment with a difficult chaotic data set, the proposed method found a much better model for the underlying dynamical process and source signals used for generating the data than the compared methods.