Reading Group: SimpleMKL

SpeakerJessica Schrouff
AffiliationUniversity of Liege / UCL
DateWednesday, 21 Aug 2013
Time16:00 - 17:30
LocationFoster Court 220
Event seriesMachine Learning for Neuroimaging Reading Group

Multiple kernel learning (MKL) aims at simultaneously learning a kernel and the associated predictor
in supervised learning settings. For the support vector machine, an efficient and general multiple
kernel learning algorithm, based on semi-infinite linear programming, has been recently proposed.
This approach has opened new perspectives since it makes MKL tractable for large-scale problems,
by iteratively using existing support vector machine code. However, it turns out that this iterative
algorithm needs numerous iterations for converging towards a reasonable solution. In this paper,
we address the MKL problem through a weighted 2-norm regularization formulation with an additional
constraint on the weights that encourages sparse kernel combinations. Apart from learning
the combination, we solve a standard SVM optimization problem, where the kernel is defined as a
linear combination of multiple kernels. We propose an algorithm, named SimpleMKL, for solving
this MKL problem and provide a new insight on MKL algorithms based on mixed-norm regularization
by showing that the two approaches are equivalent. We show how SimpleMKL can be
applied beyond binary classification, for problems like regression, clustering (one-class classification)
or multiclass classification. Experimental results show that the proposed algorithm converges
rapidly and that its efficiency compares favorably to other MKL algorithms. Finally, we illustrate
the usefulness of MKL for some regressors based on wavelet kernels and on some model selection
problems related to multiclass classification problems.

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