A unifying view of multiple kernel learning

Authors:
Marius Kloft;Ulrich Rückert;Peter L. Bartlett
Affiliations:
University of California, Berkeley and Machine Learning Group, Technische Universität Berlin, Berlin, Germany;University of California, Berkeley;University of California, Berkeley
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Year:
2010

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Abstract

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion's dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.