A globally convergent MC algorithm with an adaptive learning rate

Peng, Dezhong, Yi, Zhang, Xiang, Yong and Zhang, Haixian 2012, A globally convergent MC algorithm with an adaptive learning rate, IEEE transactions on neural networks and learning systems, vol. 23, no. 2, pp. 359-365.

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Title A globally convergent MC algorithm with an adaptive learning rate
Author(s) Peng, Dezhong
Yi, Zhang
Xiang, Yong
Zhang, Haixian
Journal name IEEE transactions on neural networks and learning systems
Volume number 23
Issue number 2
Start page 359
End page 365
Total pages 7
Publisher IEEE
Place of publication Piscataway, N. J.
Publication date 2012
ISSN 2162-237X
2162-2388
Keyword(s) deterministic discrete time system
eigenvalue
eigenvector
minor component analysis
neural networks
Summary This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30046997

Document type: Journal Article
Collection: School of Information Technology
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