Scharf, Louis L., authorAzimi-Sadjadi, Mahmood, authorPezeshki, Ali, authorElsevier Science Ltd., publisher2007-01-032007-01-032003Pezeshki, Ali, Mahmood R. Azimi-Sadjadi, and Louis L. Scharf, A Network for Recursive Extraction of Canonical Coordinates, Neural Networks 16, no. 5-6 (June-July 2003): [801]-808.http://hdl.handle.net/10217/67874A network structure for canonical coordinate decomposition is presented. The network consists of two single-layer linear subnetworks that together extract the canonical coordinates of two data channels. The connection weights of the networks are trained by a stochastic gradient descent learning algorithm. Each subnetwork features a hierarchical set of lateral connections among its outputs. The lateral connections perform a deflation process that subtracts the contributions of the already extracted coordinates from the input data subspace. This structure allows for adding new nodes for extracting additional canonical coordinates without the need for retraining the previous nodes. The performance of the network is evaluated on a synthesized data set.born digitalarticleseng©2003 Elsevier Science Ltd.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.singular value decompositioniterative learningdeflationcanonical coordinatesText