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

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2 Author(s)
S. Bannour ; Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA ; M. R. Azimi-Sadjadi

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 n th 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

Published in:

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on  (Volume:2 )

Date of Conference:

23-26 Mar 1992