Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
A neural implementation of canonical correlation analysis
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Canonical coordinates and the geometry of inference, rate, andcapacity
IEEE Transactions on Signal Processing
Wiener filters in canonical coordinates for transform coding,filtering, and quantizing
IEEE Transactions on Signal Processing
Principal component extraction using recursive least squares learning
IEEE Transactions on Neural Networks
Joint blind source separation by multiset canonical correlation analysis
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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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.