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)
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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关键词
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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