A Gneral Class of Neural Networks for Principal Component Analysis and Factor Analysis
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Generalised Canonical Correlation Analysis
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Linear prediction based blind source extraction algorithms in practical applications
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Computers & Mathematics with Applications
Study of nonlinear multivariate time series prediction based on neural networks
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Spider recognition by biometric web analysis
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Dynamical system for computing largest generalized eigenvalue
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Spider specie identification and verification based on pattern recognition of it cobweb
Expert Systems with Applications: An International Journal
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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 principal component analysis (PCA) and minor component analysis (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