Principal component extraction using recursive least squares learning Bannour, Sami ; Azimi-Sadjadi, Mahmood R. A new neural network-based approach is introduced for recursive computation of the principal components of a stationary vector stochastic process. The neurons of a single layer network are sequentially trained using a recursive least squares squares (RLS) type algorithm to extract the principal components of the input process. The optimality criterion is based on retaining the maximum information contained in the input sequence so as to be able to reconstruct the network inputs from the corresponding outputs with minimum mean squared error. The proof of the convergence of the weight vectors to the principal eigenvectors is also established. A simulation example is given to show the accuracy and speed advantages of this algorithm in comparison with the existing methods. Finally, the application of this learning algorithm to image data reduction and filtering of images degraded by additive and/or multiplicative noise is considered. Colorado State University. Libraries 1995 text ; image application/pdf ECEmra00050.pdf FACFECEN100530ARTI eng c1995 IEEE
Principal component extraction using recursive least squares learning
Bannour, Sami ; Azimi-Sadjadi, Mahmood R.
A new neural network-based approach is introduced for recursive computation of the principal components of a stationary vector stochastic process. The neurons of a single layer network are sequentially trained using a recursive least squares squares (RLS) type algorithm to extract the principal components of the input process. The optimality criterion is based on retaining the maximum information contained in the input sequence so as to be able to reconstruct the network inputs from the corresponding outputs with minimum mean squared error. The proof of the convergence of the weight vectors to the principal eigenvectors is also established. A simulation example is given to show the accuracy and speed advantages of this algorithm in comparison with the existing methods. Finally, the application of this learning algorithm to image data reduction and filtering of images degraded by additive and/or multiplicative noise is considered.
Colorado State University. Libraries
1995
text ; image
application/pdf
ECEmra00050.pdf
FACFECEN100530ARTI
eng
c1995 IEEE