L2-norm multiple kernel learning and its application to biomedical data fusion |
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| Authors: | Shi Yu Tillmann Falck Anneleen Daemen Leon-Charles Tranchevent Johan AK Suykens Bart De Moor Yves Moreau |
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| Affiliation: | (1) Bioinformatics Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee, B-3001, Belgium;(2) Systems, Models and Control Group, Department of Electrical Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Heverlee, B-3001, Belgium |
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| Abstract: | Background This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L ∞, L 1, and L 2 MKL. In particular, L 2 MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L ∞ MKL method. In real biomedical applications, L 2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources. |
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