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    陈剑挺, 叶贞成, 程辉. 基于p阶Welsch损失的鲁棒极限学习机[J]. 华东理工大学学报(自然科学版), 2020, 46(2): 243-249. DOI: 10.14135/j.cnki.1006-3080.20181209001
    引用本文: 陈剑挺, 叶贞成, 程辉. 基于p阶Welsch损失的鲁棒极限学习机[J]. 华东理工大学学报(自然科学版), 2020, 46(2): 243-249. DOI: 10.14135/j.cnki.1006-3080.20181209001
    CHEN Jianting, YE Zhencheng, CHENG Hui. Robust Extreme Learning Machine Based on p-Power Welsch Loss[J]. Journal of East China University of Science and Technology, 2020, 46(2): 243-249. DOI: 10.14135/j.cnki.1006-3080.20181209001
    Citation: CHEN Jianting, YE Zhencheng, CHENG Hui. Robust Extreme Learning Machine Based on p-Power Welsch Loss[J]. Journal of East China University of Science and Technology, 2020, 46(2): 243-249. DOI: 10.14135/j.cnki.1006-3080.20181209001

    基于p阶Welsch损失的鲁棒极限学习机

    Robust Extreme Learning Machine Based on p-Power Welsch Loss

    • 摘要: 针对极限学习机(ELM)异常值敏感的问题,提出了一种基于p阶Welsch损失的鲁棒极限学习机。使用p阶Welsch损失代替常规ELM的均方误差损失,提高算法的鲁棒性;在目标函数中引入l1范数正则项,降低ELM网络模型的复杂度,增强模型的稳定性;采用快速迭代阈值收缩算法(FISTA)极小化目标函数,提升计算效率。对人工合成数据集和部分UCI回归数据集进行仿真,实验结果表明本文方法在鲁棒性、稳定性和训练时间上都具有很好的性能。

       

      Abstract: The conventional extreme learning machine (ELM) is sensitive to outliers. Aiming at the shortcoming, this paper proposes a robust extreme learning machine based on p-power Welsch loss. Firstly, the mean square error loss of the conventional ELM is replaced by the p-power Welsch loss to enhance the robustness of the proposed algorithm; Secondly, the l1 norm regularization is introduced into the objective function to reduce the complexity and improve the stability of the ELM network model; Moreover, a fast iterative shrinkage-thresholding algorithm (FISTA) is adopted to minimize the objective function so that the computational efficiency can be increased; Finally, the performance of the proposed method is verified by means of synthetic data and UCI datasets, which shows that the proposed algorithm can attain stronger robustness, better stability and lower training time.

       

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