A neural model for adaptive Karhunen Loeve transformation (KLT)

A neural model approach to adaptively calculating the principal components of the covariance matrix of an input sequence is proposed. The algorithm is based on the successive application of the modified Hebbian learning rule proposed by E. Oja (1982) on every covariance matrix which results after ca...

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Bibliographic Details
Main Authors: Abbas, H.M., Fahmy, M.M.
Format: Conference Proceeding
Language:English
Subjects:
Covariance matrix
Data mining
Feature extraction
Hebbian theory
Karhunen-Loeve transforms
Neural networks
Neurons
Principal component analysis
Statistical analysis
Vectors
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Summary:A neural model approach to adaptively calculating the principal components of the covariance matrix of an input sequence is proposed. The algorithm is based on the successive application of the modified Hebbian learning rule proposed by E. Oja (1982) on every covariance matrix which results after calculating the previous eigenvectors. This is equivalent to removing one dimension of the orthogonal space in which the data could be represented. Adopting a modification rule for the learning rate achieves faster convergence than that obtained when using other models. The optimal learning rate is calculated by minimizing an error function of the learning rate along the gradient descent direction.< >
DOI:10.1109/IJCNN.1992.226861