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. Attached Files Name Description MIMEType Size Downloads Title A globally convergent MC algorithm with an adaptive learning rate Author(s) Peng, Dezhong Yi, Zhang Xiang, Yong orcid.org/0000-0003-3545-7863 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 Collections: Faculty of Science, Engineering and Built Environment School of Information Technology 2018 ERA Submission Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 13 Aug 2012, 12:58:26 EST Thu, 25 Oct 2012, 14:02:58 EST Thu, 25 Oct 2012, 14:04:53 EST Fri, 13 Jun 2014, 14:44:12 EST Tue, 03 Feb 2015, 13:11:03 EST Filtered Full Citation counts:   Cited 10 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 559 Abstract Views, 10 File Downloads  -  Detailed Statistics Created: Mon, 13 Aug 2012, 12:58:26 EST