Letter: Global convergence analysis of a self-stabilizing MCA learning algorithm

Authors:
Mao Ye
Affiliations:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, P.R. China
Venue:
Neurocomputing
Year:
2005

Citing 7
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Abstract

On minor component analysis (MCA) neural networks, a new algorithm is proposed. It is a self-stabilizing MCA algorithm, which means that the sign of the temporal change of the weight vector length is independent of the presented input vector. Algorithms without this property may suffer fluctuations and divergence. With suitable conditions on the initial weight vector and learning rate, a rigorous global convergence proof is given. The techniques used in the proof will be useful in many research issues such as independent component analysis, principle component analysis, etc.