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