A class of self-stabilizing MCA learning algorithms

Ye, M., Fan, X. Q. and Li, X. (2006) A class of self-stabilizing MCA learning algorithms. IEEE Transactions On Neural Networks, 17 6: 1634-1638. doi:10.1109/TNN.2006.880979

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Author Ye, M.
Fan, X. Q.
Li, X.
Title A class of self-stabilizing MCA learning algorithms
Journal name IEEE Transactions On Neural Networks   Check publisher's open access policy
ISSN 1045-9227
Publication date 2006
Sub-type Article (original research)
DOI 10.1109/TNN.2006.880979
Volume 17
Issue 6
Start page 1634
End page 1638
Total pages 5
Editor M. M. Polycarpou
Place of publication Piscataway
Publisher IEEE-Inst Electrical Electronics Engineers Inc
Collection year 2006
Language eng
Subject C1
280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic
700103 Information processing services
Abstract In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results.
Keyword Eigenvector
Feature Extraction
Global Convergence
Minor Component Analysis
Neural Networks
Computer Science, Artificial Intelligence
Computer Science, Hardware & Architecture
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Discrete-time Dynamics
Convergence Analysis
Neural-networks
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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