Abstract

Multiple kernel learning (MKL) has recently received significant attention due to the fact that it is able to automatically fuse information embedded in multiple base kernels and then find a new kernel for classification or regression. In this paper, we propose a coupled multiple kernel learning method for supervised classification (CMKL-C), which comprehensively involves the intra-coupling within each kernel, inter-coupling among different kernels and coupling between target labels and real ones in MKL. Specifically, the intra-coupling controls the class distribution in a kernel space, the inter-coupling captures the co-information of base kernel matrices, and the last type of coupling determines whether the new learned kernel can make a correct decision. Furthermore, we deduce the analytical solutions to solve the CMKL-C optimization problem for highly efficient learning. Experimental results over eight UCI data sets and three bioinformatics data sets demonstrate the superior performance of CMKL-C in terms of the classification accuracy.

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Cite this article

@article{cai-1604,
  author  = {En Zhu and Qiang Liu and Jianping Yin},
  title   = {Coupled Multiple Kernel Learning for Supervised Classification},
  journal = {Computing and Informatics},
  volume  = {36},
  number  = {3},
  pages   = {618--636},
  year    = {2017},
  issn    = {1335-9150},
  doi     = {10.4149/cai_2017_3_618}
}
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