期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 11, 期 2, 页码 529-533出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/72.839022
关键词
eigenvalue problem; learning algorithms; minor component analysis; principal component analysis
Principal component analysis (PCA) and minor component analysis (MCA) are a powerful methodology for a wide variety of applications such as pattern recognition and signal processing. In this paper, we first propose a differential equation for the generalized eigenvalue problem, We prove that the stable points of this differential equation are the eigenvectors corresponding to the largest eigenvalue, Based on this generalized differential equation, a class of PCA and MCA learning algorithms can be obtained. We demonstrate that many existing PCA and MCA learning algorithms are special cases of this class, and this class includes some new and simpler MCA learning algorithms. Our results show that all the learning algorithms of this class have the same order of convergence speed, and they are robust to implementation error.
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