Semi-supervised Learning with Regularized Laplacian
L'Apprentissage Semi-supervise avec Laplacian Regularise
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.
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