An adaptive approach for optimal data reduction using recursive least squares learning method

An approach is introduced for the recursive computation of the principal components of a vector stochastic process. The neurons of a single-layer perceptron are sequentially trained using a recursive least squares (RLS)-type algorithm to extract the principal components of the input process. The pro...

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Bibliographic Details
Main Authors: Bannour, S., Azimi-Sadjadi, M.R.
Format: Conference Proceeding
Language:English
Subjects:
Convergence
Eigenvalues and eigenfunctions
Learning systems
Least squares methods
Neural networks
Neurons
Principal component analysis
Resonance light scattering
Stochastic processes
Vectors
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Summary:An approach is introduced for the recursive computation of the principal components of a vector stochastic process. The neurons of a single-layer perceptron are sequentially trained using a recursive least squares (RLS)-type algorithm to extract the principal components of the input process. The proof of the convergence of the weights at the nth neuron to the nth principal component, given that the previous (n-1) training steps have determined the first (n-1) principal components, is established. Simulation results are given to show the accuracy and speed of this algorithm in comparison with previous methods.< >
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.1992.226061