Mixture Correntropy-Based Kernel Extreme Learning Machines

Kernel-based extreme learning machine (KELM), as a natural extension of ELM to kernel learning, has achieved outstanding performance in addressing various regression and classification problems. Compared with the basic ELM, KELM has a better generalization ability owing to no needs of the number of hidden nodes given beforehand and random projection mechanism. Since KELM is derived under the minimum mean square error (MMSE) criterion for the Gaussian assumption of noise, its performance may deteriorate under the non-Gaussian cases, seriously. To improve the robustness of KELM, this article proposes a mixture correntropy-based KELM (MC-KELM), which adopts the recently proposed maximum mixture correntropy criterion as the optimization criterion, instead of using the MMSE criterion. In addition, an online sequential version of MC-KELM (MCOS-KELM) is developed to deal with the case that the data arrive sequentially (one-by-one or chunk-by-chunk). Experimental results on regression and classification data sets are reported to validate the performance superiorities of the new methods.

Automated summary

This article introduces the kernel-based extreme learning machine (KELM) and its outstanding performance in addressing regression and classification problems. To improve the robustness of KELM, a mixture correntropy-based KELM (MC-KELM) is proposed, which adopts a maximum mixture correntropy criterion as the optimization criterion. Additionally, an online sequential version of MC-KELM (MCOS-KELM) is developed to handle sequentially arriving data. Experimental results demonstrate the superior performance of the new methods.