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Learning Non-Linear Combinations of Kernels
Corinna Cortes · Mehryar Mohri · Afshin Rostamizadeh

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @

This paper studies the general problem of learning kernels based on a polynomial combination of base kernels. It analyzes this problem in the case of regression and the kernel ridge regression algorithm. 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.

Author Information

Corinna Cortes (Google Research)
Mehryar Mohri (Google Research & Courant Institute of Mathematical Sciences)
Afshin Rostamizadeh (Google Research)

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