TY - GEN
T1 - Orthonormalization learning algorithms
AU - Hasan, Mohammed A.
PY - 2007
Y1 - 2007
N2 - Orthonormalization is an essential stabilizing task in many signal processing algorithms and can be accomplished using the Gram-Schmidt process. In this paper, dynamical systems for orthonormalization are proposed. These systems converge to the desired limits without computing matrix square root. Stability and domain of attractions are established via Lyapunov stability theory. Applications of the proposed methods to principal subspace/component analysis are given.
AB - Orthonormalization is an essential stabilizing task in many signal processing algorithms and can be accomplished using the Gram-Schmidt process. In this paper, dynamical systems for orthonormalization are proposed. These systems converge to the desired limits without computing matrix square root. Stability and domain of attractions are established via Lyapunov stability theory. Applications of the proposed methods to principal subspace/component analysis are given.
UR - http://www.scopus.com/inward/record.url?scp=51749087685&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51749087685&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2007.4371247
DO - 10.1109/IJCNN.2007.4371247
M3 - Conference contribution
AN - SCOPUS:51749087685
SN - 142441380X
SN - 9781424413805
T3 - IEEE International Conference on Neural Networks - Conference Proceedings
SP - 1894
EP - 1899
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 -