L2-norm multiple kernel learning and its application to biomedical data fusion.

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(infinity), L1, and L2 MKL. In particular, L2 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(infinity) MKL method. In real biomedical applications, L2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources.

Results

We provide a theoretical analysis of the relationship between the L2 optimization of kernels in the dual problem with the L2 coefficie

SUBMITTER: Yu S 

PROVIDER: S-EPMC2906488 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature