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Multiple Kernel Learning Method Using MRMR Criterion and Kernel Alignment

  • Peng Wu
  • Fuqing Duan
  • Ping Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)

Abstract

Multiple kernel learning (MKL) is a widely used kernel learning method, but how to select kernel is lack of theoretical guidance. The performance of MKL is depend on the users’ experience, which is difficult to choose the proper kernels in practical applications. In this paper, we propose a MKL method based on minimal redundant maximal relevance criterion and kernel alignment. The main feature of this method compared to others in the literature is that the selection of kernels is considered as a feature selection issue in the Hilbert space, and can obtain a set of base kernels with the highest relevance to the target task and the minimal redundancies among themselves. Experimental results on several benchmark classification data sets show that our proposed method can enhance the performance of MKL.

Keywords

minimal redundant maximal relevance kernel alignment kernel selection multiple kernel learning 

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References

  1. 1.
    Sonnenburg, S., Rätsch, G., Schäfer, C., Schölkopf, B.: Large scale multiple kernel learning. The Journal of Machine Learning Research 7, 1531–1565 (2006)zbMATHGoogle Scholar
  2. 2.
    Duan, L., Tsang, I.W., Xu, D., Maybank, S.J.: Domain transfer svm for video concept detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1375–1381. IEEE (2009)Google Scholar
  3. 3.
    Qi, M., Tsang, I.W.: Efficient Multi-template Learning for Structured Prediction. IEEE Transactions on Neural Networks and Learning Systems 24(2), 248–261 (2013)CrossRefGoogle Scholar
  4. 4.
    Vapnik, V.: The nature of statistical learning theory. Springer (1999)Google Scholar
  5. 5.
    Lanckriet, G.R., Cristianini, N., Bartlett, P., et al.: Learning the kernel matrix with semidefinite programming. The Journal of Machine Learning Research 5, 27–72 (2004)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Bach, F.R., Lanckriet, G.R., Jordan, M.I.: Multiple kernel learning, conic duality, and the smo algorithm. In: Proceedings of the Twenty-First International Conference on Machine Learning, pp. 41–48. ACM (2004)Google Scholar
  7. 7.
    Rakotomamonjy, A., Bach, F., Canu, S., et al.: Simplemkl. Journal of Machine Learning Research 9, 2491–2521 (2008)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Gönen, M., Alpaydin, E.: Loclized algorithms for multiple kernel learning. Pattern Recognition 46, 795–807 (2013)CrossRefzbMATHGoogle Scholar
  9. 9.
    Cortes, C., Mohri, M., Rostamizadeh, A.: Algorithms for learning kernels based on centered alignment. The Journal of Machine Learning Research 13, 795–828 (2012)MathSciNetGoogle Scholar
  10. 10.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  11. 11.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  12. 12.
    Afkanpour, A., Szepesvari, C., Bowling, M.: Alignment based kernel learning with a continuous set of base kernels. arXiv preprint arXiv:1112.4607 (2011)Google Scholar
  13. 13.
    Cristianini, N., Shawe-Taylor, J., Elisseeff, A., et al.: On kernel-target alignment. In: NIPS, pp. 367–373 (2001)Google Scholar
  14. 14.
    Bache, K., Lichman, M.: UCI machine learning repository (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peng Wu
    • 1
    • 2
  • Fuqing Duan
    • 1
  • Ping Guo
    • 1
  1. 1.Image Processing and Pattern Recognition LaboratoryBeijing Normal UniversityBeijingChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina

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