Referenced Items are not available for this document.
The minor component analysis (MCA) deals with the recovery of the eigenvector associated to the smallest eigenvalue of the autocorrelation matrix of the input dada, and it is a very important tool for signal processing and data analysis. This brief analyzes the convergence and stability of a class of self-stabilizing MCA algorithms via a deterministic discrete-time (DDT) method. Some sufficient conditions are obtained to guarantee the convergence of these learning algorithms. Simulations are carried out to further illustrate the theoretical results achieved. It can be concluded that these self-stabilizing algorithms can efficiently extract the minor component (MC), and they outperform some existing MCA methods.
Published in:
Neural Networks, IEEE Transactions on
(Volume:21
,
Issue:
1
)
Date of Publication: Jan. 2010
- Page(s):
- 175 - 181
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 11037350
- Digital Object Identifier :
- 10.1109/TNN.2009.2036725
- Product Type:
- Journals & Magazines
- Date of Publication :
- 04 December 2009
- Date of Current Version :
- 31 December 2009
- Issue Date :
- Jan. 2010
- Sponsored by :
- IEEE Computational Intelligence Society
- PubMed ID :
- 19963694
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
