A New Class of APEX-Like PCA Algorithms
نویسندگان
چکیده
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.
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