PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multiple Indefinite Kernel Learning with Mixed Norm Regularization
Matthieu Kowalski, Marie Szafranski and Liva Ralaivola
In: ICML 2009, 14-17 June 2009, Montreal, Canada.

Abstract

We address the problem of learning classifiers using several kernel functions. On the contrary to many contributions in the field of learning from different sources of information using kernels, we here do not assume that the kernels used are positive definite. The learning problem that we are interested in involves a misclassification loss term and a regularization term that is expressed by means of a mixed norm. The use of a mixed norm allows us to enforce some sparsity structure, a particular case of which is, for instance, the Group Lasso. We solve the convex problem by employing proximal minimization algorithms, which can be viewed as refined versions of gradient descent procedures capable of naturally dealing with nondifferentiability. A numerical simulation on a UCI dataset shows the modularity of our approach.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5597
Deposited By:Marie Szafranski
Deposited On:08 March 2010