Theoretical Computer Science
Global Convergence of a PCA Learning Algorithm with a Constant Learning Rate
Computers & Mathematics with Applications
Neural Information Processing
Concise Coupled Neural Network Algorithm for Principal Component Analysis
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A unified learning algorithm to extract principal and minor components
Digital Signal Processing
A family of fuzzy learning algorithms for robust principal component analysis neural networks
IEEE Transactions on Fuzzy Systems
Adaptive multiple minor directions extraction in parallel using a PCA neural network
Theoretical Computer Science
Local matrix adaptation in topographic neural maps
Neurocomputing
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A framework for a class of coupled principal component learning rules is presented. In coupled rules, eigenvectors and eigenvalues of a covariance matrix are simultaneously estimated in coupled equations. Coupled rules can mitigate the stability-speed problem affecting noncoupled learning rules, since the convergence speed in all eigendirections of the Jacobian becomes widely independent of the eigenvalues of the covariance matrix. A number of coupled learning rule systems for principal component analysis, two of them new, is derived by applying Newton's method to an information criterion. The relations to other systems of this class, the adaptive learning algorithm (ALA), the robust recursive least squares algorithm (RRLSA), and a rule with explicit renormalization of the weight vector length, are established.