Asymmetric PCA Neural Networks for
Konstantinos Diamantaras · 1998
The adaptive blind source separation problem has been traditionally dealt mith the use of nonlinear neural models implementing higher-order statistical methods. In this paper we show that second order Cross-Coupled Hebbian rille used for Asymmetric Principal Component Analysis (APCA) is capable blindly and adaptively separating uncorrelated sources. Our method en- joys the following advantages over similar higher-order models such as those performing Independent Component Analysis (ICA) : (a) the strong indepen- dence assumption about the source signals is reduced to the weaker uncor- relation assumption, (b) there is no constraint on the sources pdf's, i.e. we rctmove the assumption that at most one signal is Gaussian, and (c) the higher order statistical optimization methods are replaced with second order methods with no local minima, and(d) the kurtosis of the sources becomes ir- riblevant. Simulation experiments shows that the model successfully separates source images with kurtoses of different signs.