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

作者：Zhu, Chengzhang^*; Liu, Xinwang; Liu, Qiang; Ming, Yuewei; Yin, Jianping

来源：Mathematical Problems in Engineering, 2015, 2015: 372748.

DOI：10.1155/2015/372748

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 l(2)-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.

• 出版日期2015
• 单位中国人民解放军国防科学技术大学