Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Stochastic approximation and optimization of random systems
Stochastic approximation and optimization of random systems
On hetero-associative neural networks and adaptive interferencecancellation
IEEE Transactions on Signal Processing
Conjugate gradient eigenstructure tracking for adaptive spectralestimation
IEEE Transactions on Signal Processing
Reduced-dimension blind space-time 2-D RAKE receivers for DS-CDMAcommunication systems
IEEE Transactions on Signal Processing
Self-organizing algorithms for generalized eigen-decomposition
IEEE Transactions on Neural Networks
Against the convergence of the minor component analysis neurons
IEEE Transactions on Neural Networks
Algorithms for accelerated convergence of adaptive PCA
IEEE Transactions on Neural Networks
The MCA EXIN neuron for the minor component analysis
IEEE Transactions on Neural Networks
Energy function for the one-unit Oja algorithm
IEEE Transactions on Neural Networks
Theoretical Computer Science
Global Convergence of a PCA Learning Algorithm with a Constant Learning Rate
Computers & Mathematics with Applications
IEEE Transactions on Signal Processing
A family of fuzzy learning algorithms for robust principal component analysis neural networks
IEEE Transactions on Fuzzy Systems
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
Adaptive multiple minor directions extraction in parallel using a PCA neural network
Theoretical Computer Science
Convergence proof of matrix dynamics for online linear discriminant analysis
Journal of Multivariate Analysis
An incremental linear discriminant analysis using fixed point method
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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The paper presents a unified framework to derive and analyze 10 different adaptive algorithms, some well-known, to compute the first principal eigenvector of the correlation matrix of a random vector sequence. Since adaptive principal eigenvector algorithms have originated from a diverse set of disciplines, including ad hoc methods, it is necessary to examine them in a unified framework. In a common framework consisting of five steps, we analyze the derivation, convergence, and rate results for many well-known algorithms as well as two new adaptive algorithms. In the process, we offer fresh perspectives on the known algorithms, and derive new results for others. The common framework also allows us to comparatively study the 10 algorithms. Finally, we show experimental results to support our analyses.