Coupled Multiple Kernel Learning for Supervised Classification

Year, pages: 2017, 618 - 636

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.

Zhu, E., Liu, Q., Yin, J. 2017. Coupled Multiple Kernel Learning for Supervised Classification. In Computing and Informatics, vol. 36, no.3, pp. 618-636. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_3_618

Zhu, E., Liu, Q., Yin, J. (2017). Coupled Multiple Kernel Learning for Supervised Classification. Computing and Informatics, 36(3), 618-636. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_3_618