A new class of APEX-like PCA algorithms

Author

Fiori, Simone ; Uncini, Aurelio ; Piazza, Francesco

Author_Institution

Dipt. di Elettronica e Autom., Ancona Univ., Italy

Volume

3

fYear

1998

Firstpage

66

Abstract

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

Keywords

data analysis; learning (artificial intelligence); neural net architecture; signal processing; APEX-like PCA algorithms; adaptive principal component extractor; laterally-connected neural architecture; laterally-connected topology; learning rules; neural principal component analysis; optimization theory; real-valued random signals; statistical data analysis technique; Convergence; Covariance matrix; Data analysis; Measurement uncertainty; Neural networks; Power measurement; Principal component analysis; Signal analysis; Topology; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1998. ISCAS '98. Proceedings of the 1998 IEEE International Symposium on

Conference_Location

Monterey, CA

Print_ISBN

0-7803-4455-3

Type

conf

DOI

10.1109/ISCAS.1998.703898

Filename

703898