More Efficiency in Multiple Kernel Learning
Alain Rakotomamonjy, Francis Bach, Yves Grandvalet and Stéphane Canu
In: ICML 2007, 20-24 Jun 2007, Corvallis, USA.

## Abstract

An efficient and general multiple kernel learning (MKL) algorithm has been recently proposed by \singleemcite{sonnenburg_mkljmlr}. This approach has opened new perspectives since it makes the MKL approach tractable for large-scale problems, by iteratively using existing support vector machine code. However, it turns out that this iterative algorithm needs several iterations before converging towards a reasonable solution. In this paper, we address the MKL problem through an adaptive 2-norm regularization formulation. Weights on each kernel matrix are included in the standard SVM empirical risk minimization problem with a $\ell_1$ constraint to encourage sparsity. We propose an algorithm for solving this problem and provide an new insight on MKL algorithms based on block 1-norm regularization by showing that the two approaches are equivalent. Experimental results show that the resulting algorithm converges rapidly and its efficiency compares favorably to other MKL algorithms.

EPrint Type: Conference or Workshop Item (Talk) Project Keyword UNSPECIFIED Learning/Statistics & OptimisationTheory & Algorithms 3509 Yves Grandvalet 11 February 2008