Learning Non-Linear Combinations of Kernels

This paper studies the general problem of learning kernels based on a polynomial combination of base kernels. It examines the corresponding learning kernel optimization problem, shows how that minimax problem can be reduced to a simpler minimization problem, and proves that the global solution of this problem always lies on the boundary. We give a projection-based gradient descent algorithm for solving the optimization problem, shown empirically to converge in few iterations. Finally, we report the results of extensive experiments with this algorithm using several publicly available datasets demonstrating the effectiveness of our technique. Papers published at the Neural Information Processing Systems Conference.