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

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, 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 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 coefficient regularization in the primal problem. Understanding the dual L2 problem grants a unified view on MKL and enables us to extend the L2 method to a wide range of machine learning problems. We implement L2 MKL for ranking and classif...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Shi Yu, Tillmann Falck, Anneleen Daemen, Léon-Charles Tranchevent, Johan A. K. Suykens, Bart De Moor, Yves Moreau
• Keywords: BMCBI 2010, Data Sources, Multiple Kernel, Support Vector Machine