  For semi-supervised learning on graphs, we study how initial kernels in a
supervised learning regime can be augmented with additional information from
known priors or from unsupervised learning outputs. These augmented kernels are
constructed in a simple update scheme based on the Schur-Hadamard product of
the kernel with additional feature kernels. As generators of the positive
definite kernels we will focus on graph basis functions (GBF) that allow to
include geometric information of the graph via the graph Fourier transform.
Using a regularized least squares (RLS) approach for machine learning, we will
test the derived augmented kernels for the classification of data on graphs.
