PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

More efficiency in multiple kernel learning
Alain Rakotomamonjy, Stéphane Canu and Yves Grandvalet
(2006) Technical Report. INSA Ruen, Rouen, France.

Abstract

Recently, an efficient and general multiple kernel learning (MKL) al- gorithm has been proposed . This approach has opened new perspectives since it has turned the MKL approach tractable for large-scale problems. However, it turns out that this iterative algorithm needs several iterations before converging towards a reasonable solution. In this work, we address this MKL problem through an adaptive 2-norm regularization formula- tion. Weights on each kernel matrix are included in the standard SVM empirical risk minimization problem and their sparsity has been forced by means of an appropriate ℓ1 constraints. 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 con- verges rapidly and its efficiency compares favorably to the state-of-the-art multiple kernel algorithm.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2886
Deposited By:Alain Rakotomamonjy
Deposited On:23 November 2006