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Mathematical Problems in Engineering
Volume 2015, Article ID 372748, 9 pages
Research Article

Distance Based Multiple Kernel ELM: A Fast Multiple Kernel Learning Approach

1College of Computer, National University of Defense Technology, Changsha 410073, China
2State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China

Received 20 August 2014; Revised 7 November 2014; Accepted 10 November 2014

Academic Editor: Tao Chen

Copyright © 2015 Chengzhang Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a distance based multiple kernel extreme learning machine (DBMK-ELM), which provides a two-stage multiple kernel learning approach with high efficiency. Specifically, DBMK-ELM first projects multiple kernels into a new space, in which new instances are reconstructed based on the distance of different sample labels. Subsequently, an -norm regularization least square, in which the normal vector corresponds to the kernel weights of a new kernel, is trained based on these new instances. After that, the new kernel is utilized to train and test extreme learning machine (ELM). Extensive experimental results demonstrate the superior performance of the proposed DBMK-ELM in terms of the accuracy and the computational cost.