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A neural networks learning algorithm for minor component analysis and its convergence analysis

journal contribution
posted on 2008-03-01, 00:00 authored by D Peng, Z Yi, J Lv, Yong XiangYong Xiang
The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.

History

Journal

Neurocomputing

Volume

71

Issue

7-9

Pagination

1748 - 1752

Publisher

Elsevier BV

Location

Amsterdam, Netherlands

ISSN

0925-2312

eISSN

1872-8286

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2007, Elsevier B.V