Alignment Based Kernel Learning with a Continuous Set of Base Kernels

TL;DR

This paper introduces a novel kernel learning algorithm that efficiently combines a continuous set of base kernels, achieving state-of-the-art results and enabling more flexible kernel parameterizations without discretization.

Contribution

The paper presents a new algorithm for continuous kernel set combination that reduces computational complexity and improves kernel selection flexibility.

Findings

Achieves state-of-the-art performance on real-world datasets.

Requires less computation than previous continuous kernel methods.

Effectively handles multiple dimensional kernel parameterizations.

Abstract

The success of kernel-based learning methods depend on the choice of kernel. Recently, kernel learning methods have been proposed that use data to select the most appropriate kernel, usually by combining a set of base kernels. We introduce a new algorithm for kernel learning that combines a {\em continuous set of base kernels}, without the common step of discretizing the space of base kernels. We demonstrate that our new method achieves state-of-the-art performance across a variety of real-world datasets. Furthermore, we explicitly demonstrate the importance of combining the right dictionary of kernels, which is problematic for methods based on a finite set of base kernels chosen a priori. Our method is not the first approach to work with continuously parameterized kernels. However, we show that our method requires substantially less computation than previous such approaches, and so is more amenable to multiple dimensional parameterizations of base kernels, which we demonstrate.