On the discrete time dynamics of a self-stabilizing MCA learning algorithm

Peng, Dezhong, Yi, Zhang and Xiang, Yong 2008, On the discrete time dynamics of a self-stabilizing MCA learning algorithm, Mathematical and computer modelling, vol. 47, no. 9-10, pp. 903-916.

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Title On the discrete time dynamics of a self-stabilizing MCA learning algorithm
Author(s) Peng, Dezhong
Yi, Zhang
Xiang, Yong
Journal name Mathematical and computer modelling
Volume number 47
Issue number 9-10
Start page 903
End page 916
Total pages 14
Publisher Pergamon
Place of publication Oxford, England
Publication date 2008-05
ISSN 0895-7177
1872-9479
Keyword(s) minor component analysis (MCA)
deterministic discrete time (DDT) system
eigenvector
eigenvalue
Summary The stability of minor component analysis (MCA) learning algorithms is an important problem in many signal processing applications. In this paper, we propose an effective MCA learning algorithm that can offer better stability. The dynamics of the proposed algorithm are analyzed via a corresponding deterministic discrete time (DDT) system. It is proven that if the learning rate satisfies some mild conditions, almost all trajectories of the DDT system starting from points in an invariant set are bounded, and will converge to the minor component of the autocorrelation matrix of the input data. Simulation results will be furnished to illustrate the theoretical results achieved.
Language eng
Field of Research 090609 Signal Processing
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2008
Copyright notice ©2007, Elsevier Ltd
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017546

Document type: Journal Article
Collection: School of Engineering and Information Technology
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