Geodesic
Convergence Analysis for Principal Component Flows
International Journal of Applied Mathematics and Computer Science, Tome 11 (2001) no. 1, pp. 223-236.

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A common framework for analyzing the global convergence of several flows for principal component analysis is developed. It is shown that flows proposed by Brockett, Oja, Xu and others are all gradient flows and the global convergence of these flows to single equilibrium points is established. The signature of the Hessian at each critical point is determined.
Keywords: principal component analysis, neural networks, gradient flows, phase portrait, Hessians
Mots-clés : badanie zbieżności, sieć neuronowa 
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     author = {Yoshizawa, S. and Helmke, U. and Starkov, K.},
     title = {Convergence {Analysis} for {Principal} {Component} {Flows}},
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Yoshizawa, S.; Helmke, U.; Starkov, K. Convergence Analysis for Principal Component Flows. International Journal of Applied Mathematics and Computer Science, Tome 11 (2001) no. 1, pp. 223-236. http://geodesic.mathdoc.fr/item/IJAMCS_2001_11_1_a9/