  We study a semi-supervised learning method based on the similarity graph and
RegularizedLaplacian. We give convenient optimization formulation of the
Regularized Laplacian method and establishits various properties. In
particular, we show that the kernel of the methodcan be interpreted in terms of
discrete and continuous time random walks and possesses several
importantproperties of proximity measures. Both optimization and linear algebra
methods can be used for efficientcomputation of the classification functions.
We demonstrate on numerical examples that theRegularized Laplacian method is
competitive with respect to the other state of the art semi-supervisedlearning
methods.
