Convergence analysis of deterministic discrete time system of a unified self-stabilizing algorithm for PCA and MCA.

Neural networks : the official journal of the International Neural Network Society(2012)

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摘要
Unified algorithms for principal and minor components analysis can be used to extract principal components and if altered simply by the sign, it can also serve as a minor component extractor. Obviously, the convergence of these algorithms is an essential issue in practical applications. This paper studies the convergence of a unified PCA and MCA algorithm via a corresponding deterministic discrete-time (DDT) system and some sufficient conditions to guarantee convergence are obtained. Simulations are carried out to further illustrate the theoretical results achieved.
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corresponding deterministic discrete-time,essential issue,mca algorithm,unified self-stabilizing algorithm,deterministic discrete time system,minor components analysis,paper study,principal component,convergence analysis,minor component extractor,sufficient condition,unified algorithm,practical application,neural networks,feature extraction
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