A stable MCA learning algorithm

Peng, Dezhong, Yi, Zhang, Lv, Jian Cheng and Xiang, Yong 2008, A stable MCA learning algorithm, Computers and Mathematics with Applications, vol. 56, no. 4, pp. 847-860, doi: 10.1016/j.camwa.2008.01.016.

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Title A stable MCA learning algorithm
Author(s) Peng, Dezhong
Yi, Zhang
Lv, Jian Cheng
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Journal name Computers and Mathematics with Applications
Volume number 56
Issue number 4
Start page 847
End page 860
Total pages 14
Publisher Pergamon
Place of publication Oxford, England
Publication date 2008-08
ISSN 0898-1221
Keyword(s) neural networks
minor component analysis (MCA)
deterministic discrete time (DDT) system
Summary Minor component analysis (MCA) is an important statistical tool for signal processing and data analysis. Neural networks can be used to extract online minor component from input data. Compared with traditional algebraic  approaches, a neural network method has a lower computational complexity. Stability of neural networks learning algorithms is crucial to practical applications. In this paper, we propose a stable MCA neural networks learning algorithm, which has a more satisfactory numerical stability than some existing MCA algorithms. Dynamical behaviors of the proposed algorithm are analyzed via deterministic discrete time (DDT) method and the conditions are obtained to guarantee convergence. Simulations are carried out to illustrate the theoretical results achieved.
Language eng
DOI 10.1016/j.camwa.2008.01.016
Field of Research 090609 Signal Processing
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2008
Copyright notice ©2008, Elsevier Ltd.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017547

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