A network for recursive extraction of canonical coordinates
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
A likelihood-based decision feedback system for multi-aspect classification of underwater targets
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Underwater target detection from multi-platform sonar imagery using multi-channel coherence analysis
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Properness and widely linear processing of quaternion random vectors
IEEE Transactions on Information Theory
Detection of spatially correlated Gaussian time series
IEEE Transactions on Signal Processing
Testing blind separability of complex Gaussian mixtures
Signal Processing
| Hi-index | 35.75 |
Canonical correlations measure the cosines of principal angles between random vectors. These cosines multiplicatively decompose concentration ellipses for second-order filtering and additively decompose the information rate for the Gaussian channel. More over, they establish a geometrical connection between error covariance, error rate, information rate, and principal angles. There is a limit to how small these angles can be made, and this limit determines the channel capacity