A network for recursive extraction of canonical coordinates
Date
2003
Authors
Scharf, Louis L., author
Azimi-Sadjadi, Mahmood, author
Pezeshki, Ali, author
Elsevier Science Ltd., publisher
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Abstract
A 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.
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Subject
singular value decomposition
iterative learning
deflation
canonical coordinates