title: A network for recursive extraction of canonical coordinates.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA. ali@engr.colostate.edu
authors.forename: Ali
authors.initials: A
authors.lastname: Pezeshki
authors.forename: Mahmood R
authors.initials: MR
authors.lastname: Azimi-Sadjadi
authors.forename: Louis L
authors.initials: LL
authors.lastname: Scharf
mesh_terms: D007256:Information Systems; D016571:Neural Networks, Computer