Singular value decomposition learning on double Stiefel manifold
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1Faculty of Engineering, Perugia University, Loc. Pentima bassa, 21, I-05100 Terni, Italy. sfr@unipg.it
International Journal of Neural Systems
|July 29, 2003
Abstract:
The aim of this paper is to present a unifying view of four SVD-neural-computation techniques found in the scientific literature and to present some theoretical results on their behavior. The considered SVD neural algorithms are shown to arise as Riemannian-gradient flows on double Stiefel manifold and their geometric and dynamical properties are investigated with the help of differential geometry.
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