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)


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.


minimal redundant maximal relevance kernel alignment kernel selection multiple kernel learning 


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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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