A Modified Oja–Xu MCA Learning Algorithm and Its Convergence Analysis

Author

Peng, Dezhong ; Yi, Zhang

Author_Institution

Comput. Intelligence Lab., Univ. of Electron. Sci. & Technol. of China, Chengdu

Volume

54

Issue

4

fYear

2007

fDate

4/1/2007 12:00:00 AM

Firstpage

348

Lastpage

352

Abstract

The original Oja-Xu minor component analysis (MCA) learning algorithm is not convergent. This brief shows that by modifying Oja-Xu MCA learning algorithm with a normalization step the modified one could be convergent subject to some conditions satisfied. The convergence of the modified MCA learning algorithm is studied by analyzing the convergence of an associated deterministic discrete time system. Necessary and sufficient conditions for convergence are obtained. Simulations further confirm the results

Keywords

convergence; discrete time systems; eigenvalues and eigenfunctions; learning (artificial intelligence); Oja-Xu MCA learning algorithm; convergence analysis; deterministic discrete time system; eigenvalue; eigenvector; minor component analysis; modified MCA learning algorithm; neural networks; Algorithm design and analysis; Convergence; Data mining; Discrete cosine transforms; Neural networks; Neurons; Signal processing algorithms; Stochastic processes; Sufficient conditions; Vectors; Deterministic discrete time (DDT) system; eigenvalue; eigenvector; minor component analysis (MCA); neural networks;

fLanguage

English

Journal_Title

Circuits and Systems II: Express Briefs, IEEE Transactions on

Publisher

ieee

ISSN

1549-7747

Type

jour

DOI

10.1109/TCSII.2006.889709

Filename

4155069