A Hierarchical Learning Rule for Independent Component Analysis

In this paper, a two-layer neural network is presented that organizes itself to perform Independent Component Analysis (ICA). A hierarchical, nonlinear learning rule is proposed which allows to extract the unknown independent source signals out of a linear mixture. The convergence behaviour of the network is analyzed mathematically.