TY - GEN
T1 - Adaptive diagonalization for canonical correlation analysis
AU - Hasan, Mohammed A.
PY - 2007
Y1 - 2007
N2 - Canonical correlation analysis is an essential technique in many fields such as multivariate statistical analysis and signal processing. In this paper, un-constrained optimization criteria for extracting the actual canonical correlation coordinates are proposed. The resulting gradient dynamical system is thoroughly analyzed in terms of stability and the limiting behavior of the system as t → ∞. One of the main features of this approach is that orthogonal basis for canonical variates which diagonalizes the coherence matrix is automatically obtained. A numerical example is included to demonstrate the performance of the proposed algorithm.
AB - Canonical correlation analysis is an essential technique in many fields such as multivariate statistical analysis and signal processing. In this paper, un-constrained optimization criteria for extracting the actual canonical correlation coordinates are proposed. The resulting gradient dynamical system is thoroughly analyzed in terms of stability and the limiting behavior of the system as t → ∞. One of the main features of this approach is that orthogonal basis for canonical variates which diagonalizes the coherence matrix is automatically obtained. A numerical example is included to demonstrate the performance of the proposed algorithm.
UR - http://www.scopus.com/inward/record.url?scp=51749121236&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51749121236&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2007.4371249
DO - 10.1109/IJCNN.2007.4371249
M3 - Conference contribution
AN - SCOPUS:51749121236
SN - 142441380X
SN - 9781424413805
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 1906
EP - 1911
BT - The 2007 International Joint Conference on Neural Networks, IJCNN 2007 Conference Proceedings
T2 - 2007 International Joint Conference on Neural Networks, IJCNN 2007
Y2 - 12 August 2007 through 17 August 2007
ER -