Orthonormalization Learning Algorithms
Mohammed A. Hasan · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · 2007
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 sub space /component analysis are given.