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

Peng, Dezhong, Yi, Zhang, Lv, JianCheng and Xiang, Yong 2008, A neural networks learning algorithm for minor component analysis and its convergence analysis, Neurocomputing, vol. 71, no. 7-9, pp. 1748-1752.

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Title A neural networks learning algorithm for minor component analysis and its convergence analysis
Author(s) Peng, Dezhong
Yi, Zhang
Lv, JianCheng
Xiang, Yong
Journal name Neurocomputing
Volume number 71
Issue number 7-9
Start page 1748
End page 1752
Publisher Elsevier BV
Place of publication Amsterdam, Netherlands
Publication date 2008-03
ISSN 0925-2312
1872-8286
Keyword(s) minor component analysis (MCA)
deterministic discrete time (DDT) system
eigenvalue
eigenvector
Summary 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.
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 B.V
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017545

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