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Orthonormalization Learning Algorithms | IEEE Conference Publication | IEEE Xplore

Orthonormalization Learning Algorithms


Abstract:

Orthonormalization is an essential stabilizing task in many signal processing algorithms and can be accomplished using the Gram-Schmidt process. In this paper, dynamical ...Show More

Abstract:

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.
Date of Conference: 12-17 August 2007
Date Added to IEEE Xplore: 29 October 2007
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Conference Location: Orlando, FL, USA

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