Abstract : We study a semi-supervised learning method based on the similarity graph and Regularized
Laplacian. We give convenient optimization formulation of the Regularized Laplacian method and establish
its various properties. In particular, we show that the kernel of the method
can be interpreted in terms of discrete and continuous time random walks and possesses several important
properties of proximity measures. Both optimization and linear algebra methods can be used for efficient
computation of the classification functions. We demonstrate on numerical examples that the
Regularized Laplacian method is competitive with respect to the other state of the art semi-supervised
learning methods.
https://hal.inria.fr/hal-01184812
Contributeur : Konstantin Avrachenkov
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Soumis le : lundi 17 août 2015 - 18:52:10
Dernière modification le : dimanche 25 février 2018 - 14:48:02
Document(s) archivé(s) le : mercredi 18 novembre 2015 - 12:19:10
