Annals of Mathematics and Artificial Intelligence

, Volume 69, Issue 3, pp 225–241 | Cite as

Analytic center cutting plane method for multiple kernel learning

  • Sharon Wulff
  • Cheng Soon Ong


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.


Multiple kernel learning accpm Oracle methods Machine learning 

Mathematics Subject Classification (2010)



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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceETHZürichSwitzerland
  2. 2.NICTAThe University of MelbourneMelbourneAustralia

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