  We develop a multi-kernel based regression method for graph signal processing
where the target signal is assumed to be smooth over a graph. In multi-kernel
regression, an effective kernel function is expressed as a linear combination
of many basis kernel functions. We estimate the linear weights to learn the
effective kernel function by appropriate regularization based on graph
smoothness. We show that the resulting optimization problem is shown to be
convex and pro- pose an accelerated projected gradient descent based solution.
Simulation results using real-world graph signals show efficiency of the
multi-kernel based approach over a standard kernel based approach.
