A modular realization of adaptive PCA

Author

Shirazi, Malltlad S. ; Noda, Hideki ; Sawai, Hidefumi

Author_Institution

Commun. Res. Lab., Minist. of Posts & Telecommun., Japan

Volume

4

fYear

1997

Firstpage

3053

Abstract

We propose an adaptive PCA algorithm which alleviates suboptimality of the PCA method for nonstationary signals. A modular neural realization of adaptive PCA is considered and its design is formulated as an optimization problem, following the design of the vector quantizer. This formulation results in a competitive algorithm that learns data´s local eigenstructures in an unsupervised way. The algorithm includes the recently proposed adaptive transform coding algorithm of R.D. Dony and S. Haykin (1995) as a special case and, as confirmed by simulation studies, the algorithm is better than their algorithm in mean square error (MSE)

Keywords

adaptive systems; competitive algorithms; eigenvalues and eigenfunctions; neural nets; statistical analysis; unsupervised learning; vector quantisation; MSE; adaptive PCA algorithm; adaptive transform coding algorithm; competitive algorithm; local eigenstructures; mean square error; modular neural realization; modular realization; nonstationary signals; optimization problem; principal component analysis; simulation studies; statistical technique; suboptimality; unsupervised learning; vector quantizer; Data compression; Decorrelation; Feature extraction; Laboratories; Mean square error methods; Principal component analysis; Random processes; Stability; Statistics; Transform coding;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on

ISSN

1062-922X

Print_ISBN

0-7803-4053-1

Type

conf

DOI

10.1109/ICSMC.1997.633055

Filename

633055