The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold
Neural Computation
Projection approximation subspace tracking
IEEE Transactions on Signal Processing
Generalized weighted rules for principal components tracking
IEEE Transactions on Signal Processing
A new simple ∞OH neuron model as a biologically plausible principal component analyzer
IEEE Transactions on Neural Networks
A "nonnegative PCA" algorithm for independent component analysis
IEEE Transactions on Neural Networks
Coupled principal component analysis
IEEE Transactions on Neural Networks
Discriminative components of data
IEEE Transactions on Neural Networks
Auditory learning: a developmental method
IEEE Transactions on Neural Networks
Blind information-theoretic multiuser detection algorithms for DS-CDMA and WCDMA downlink systems
IEEE Transactions on Neural Networks
Modulated Hebb-Oja learning Rule-a method for principal subspace analysis
IEEE Transactions on Neural Networks
Learning in linear neural networks: a survey
IEEE Transactions on Neural Networks
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This paper presents Modified Modulated Hebb-Oja (MHO) method that performs principal component analysis. Method is based on implementation of Time-Oriented Hierarchical Method applied on recently proposed principal subspace analysis rule called Modulated Hebb-Oja learning rule. Comparing to some other well-known methods for principal component analysis, the proposed method has one feature that could be seen as desirable from the biological point of view --- synaptic efficacy learning rule does not need the explicit information about the value of the other efficacies to make individual efficacy modification. Simplicity of the "neural circuits" that perform global computations and a fact that their number does not depend on the number of input and output neurons, could be seen as good features of the proposed method. The number of necessary global calculation circuit is one. Some similarity to a part of the frog retinal circuit will be suggested, too.