Simple Neuron Models for Independent Component Analysis

摘要

Recently, several neural algorithms have been introduced for IndependentComponent Analysis. Here we approach the problem from the point of view of a single neuron. First, a general class of simple Hebbian-like learning rules are introduced for estimating one of the independent components from sphered data. Some of the learning rules can be used to estimate an independent component which has a negative kurtosis and the others estimate a component of positive kurtosis. Next, a two-unit system is introduced to estimate an independent component of any kurtosis. The results are then generalized to estimate independent components from non-sphered (raw) mixtures. To separate several independent components, a system of several neuron with linear negative feedback is used. The convergence of the learning rules is rigorously proven without any unnecessary hypotheses on the distributions of the independent components. (Copyright (c) 1996 Helsinki University of Technology.)