Simple and Efficient Multiple Kernel Learning by Group Lasso

We consider the problem of how to improve the efficiency of Multiple Kernel Learning (MKL). In literature, MKL is often solved by an alternating approach: (1) the minimization of the kernel weights is solved by complicated techniques, such as Semi-infinite Linear Programming, Gradient Descent, or Level method; (2) the maximization of SVM dual variables can be solved by standard SVM solvers. However, the minimization step in these methods is usually dependent on its solving techniques or commercial softwares, which therefore limits the efficiency and applicability. In this paper, we formulate a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL. Although this equivalence is not our invention, our derived variant equivalence not only leads to an efficient algorithm for MKL, but also generalizes to the case for Lp-MKL (p 1 and denoting the Lp-norm of kernel weights). Therefore, our proposed algorithm provides a unified solution fo...

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Michael R. Lyu
• Keywords: ICML 2010, Kernel Weights, Machine Learning, Standard Svm Solvers, SVM Dual Variables