More generality in efficient multiple kernel learning

It is observed that existing MKL formulations can be extended to learn general kernel combinations subject to general regularization while retaining all the efficiency of existing large scale optimization algorithms.

Abstract

Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning tasks. The development of efficient gradient descent based optimization schemes has made it possible to tackle large scale problems. Simultaneously, MKL based algorithms have achieved very good results on challenging real world applications. Yet, despite their successes, MKL approaches are limited in that they focus on learning a linear combination of given base kernels.