A new class of APEX-like PCA algorithms
Simone Fiori, Aurelio Uncini, Francesco Piazza · 2002
One of the most commonly known algorithm to perform neural Principal Component Analysis of real-valued random signals is the Kung-Diamantaras' Adaptive Principal component EXtractor (APEX) for a laterally-connected neural architecture. In this paper we present a new approach to obtain an APEX-like PCA procedure as a special case of a more general class of learning rules, by means of an optimization theory specialized for the laterally-connected topology. Through simulations we show the new algorithms can be faster than the original one.