Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A statistical framework for genomic data fusion
Bioinformatics
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
An Automated Combination of Kernels for Predicting Protein Subcellular Localization
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
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Multiple Kernel Learning (MKL) is a popular generalization of kernel methods which allows the practitioner to optimize over convex combinations of kernels. We observe that many recent MKL solutions can be cast in the framework of oracle based optimization, and show that they vary in terms of query point generation. The popularity of such methods is because the oracle can fortuitously be implemented as a support vector machine. Motivated by the success of centering approaches in interior point methods, we propose a new approach to optimize the MKL objective based on the analytic center cutting plane method (accpm). Our experimental results show that accpm outperforms state of the art in terms of rate of convergence and robustness. Further analysis sheds some light as to why MKL may not always improve classification accuracy over naive solutions.