Azimi-Sadjadi, Mahmood R., authorBannour, Sami, authorIEEE, publisher2007-01-032007-01-031995Bannour, Sami and Mahmood R. Azimi-Sadjadi, Principal Component Extraction Using Recursive Least Squares Learning, IEEE Transactions on Neural Networks 6, no. 2 (March 1995): 457-469.http://hdl.handle.net/10217/921A 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.born digitalarticleseng©1995 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.data reductionfiltering theorystochastic processesleast squares approximationslearning (artificial intelligence)neural netsText