A general class of psi-APEX PCA neural algorithms

Principal component analysis (PCA) can be successfully applied to a variety of signal processing problems. Different analyzers have been reported in t he scientific literature; among others, the Adaptive Principal component EX tractor (APEX) by Kung and Diamantaras has attracted much interest in the s cientific community since it involves a specific neural architecture and a specific learning theory. The aim of this brief is to present a general cla ss of APEX-like learning rules (referred to as psi -APEX) and to illustrate their features by theoretical and numerical analysis.