Theoretical Computer Science
A power-based adaptive method for eigenanalysis without square-root operations
Digital Signal Processing
Global Convergence of a PCA Learning Algorithm with a Constant Learning Rate
Computers & Mathematics with Applications
Neurocomputing
Concise Coupled Neural Network Algorithm for Principal Component Analysis
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Local Dimensionality Reduction for Non-Parametric Regression
Neural Processing Letters
A robust and globally convergent PCA learning algorithm
Control and Intelligent Systems
Linear mltilayer ICA using adaptive PCA
Neural Processing Letters
Unsupervised learning of a kinematic arm model
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A family of fuzzy learning algorithms for robust principal component analysis neural networks
IEEE Transactions on Fuzzy Systems
A principal components analysis neural gas algorithm for anomalies clustering
WSEAS TRANSACTIONS on SYSTEMS
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
Face recognition using difference vector plus KPCA
Digital Signal Processing
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A learning algorithm for the principal component analysis (PCA) is developed based on the least-square minimization. The dual learning rate parameters are adjusted adaptively to make the proposed algorithm capable of fast convergence and high accuracy for extracting all principal components. The proposed algorithm is robust to the error accumulation existing in the sequential PCA algorithm. We show that all information needed for PCA can he completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The updating of the normalized weight vector can be referred to as a leaky Hebb's rule. The convergence of the proposed algorithm is briefly analyzed. We also establish the relation between Oja's rule and the least squares learning rule. Finally, the simulation results are given to illustrate the effectiveness of this algorithm for PCA and tracking time-varying directions-of-arrival