Singular value decomposition learning on double Stiefel manifold.
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.
Fiori
Simone
S
Faculty of Engineering, Perugia University, Loc. Pentima bassa, 21, I-05100 Terni, Italy. sfr@unipg.it
D000465:Algorithms; D006801:Humans; D007858:Learning; D008959:Models, Neurological; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics