Bayesian efficient multiple kernel learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Scopus citations

Abstract

Multiple kernel learning algorithms are proposed to combine kernels in order to obtain a better similarity measure or to integrate feature representations coming from different data sources. Most of the previous research on such methods is focused on the computational efficiency issue. However, it is still not feasible to combine many kernels using existing Bayesian approaches due to their high time complexity. We propose a fully conjugate Bayesian formulation and derive a deterministic variational approximation, which allows us to combine hundreds or thousands of kernels very efficiently. We briefly explain how the proposed method can be extended for multiclass learning and semi-supervised learning. Experiments with large numbers of kernels on benchmark data sets show that our inference method is quite fast, requiring less than a minute. On one bioinformatics and three image recognition data sets, our method outperforms previously reported results with better generalization performance.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Pages1-8
Number of pages8
StatePublished - Oct 10 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: Jun 26 2012Jul 1 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012
Volume1

Other

Other29th International Conference on Machine Learning, ICML 2012
CountryUnited Kingdom
CityEdinburgh
Period6/26/127/1/12

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

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  • Cite this

    Gönen, M. (2012). Bayesian efficient multiple kernel learning. In Proceedings of the 29th International Conference on Machine Learning, ICML 2012 (pp. 1-8). (Proceedings of the 29th International Conference on Machine Learning, ICML 2012; Vol. 1).