Multi-scale Gaussian Kernel Extreme Learning MachineBased on Maximum Correntropy Criterion
LIU Zhao-lun1,2,WU You2,WANG Wei-tao3,ZHANG Chun-lan2,LIU Bin1,2
1. The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China
2. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
3. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract：In view of the fact that the traditional multi-scale kernel extreme learning machine is sensitive to noise and has a large amount of computation, a multi-scale kernel extreme learning machine which is suitable for Gaussian noise environment is proposed. Firstly, the maximum correntropy criterion is used to replace the traditional minimum mean square error criterion in the multi-scale kernel extreme learning machine to construct the objective function. Secondly, a multi-scale method for randomly generating the scale factors according to the training samples number is applied to the Gaussian kernel function. Finally, the Lagrange multiplier method is used to solve the objective function, and the multi-scale Gaussian kernel extreme learning machine based on the maximum correntropy criterion is derived. Experiments show that the proposed algorithm has higher learning efficiency. Comparing with the traditional multi-scale kernel extreme learning machine, the prediction accuracy on the three UCI benchmark data sets and the application experiment for predicting the f-CaO content of cement clinker,increased by an average of 30.30% and 23.8% respectively.
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2021, Vol. 42 
