基于M-estimator的鲁棒宽度学习系统
作者:
作者单位:

1.盐城师范学院;2.南京航空航天大学

作者简介:

通讯作者:

中图分类号:

TP183

基金项目:

国家自然科学基金项目(61603326);江苏省心理与认知科学大数据重点建设实验室开放基金(72591962004G)


M-estimator-based Robust Broad Learning System
Author:
Affiliation:

Yancheng Teachers University

Fund Project:

National Natural Science Foundation of China (Grant No. 61603326); Research fund of Jiangsu Provincial Key Constructive Laboratory for Big Data of Psychology and Cognitive Science (Grant No. 72591962004G)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    宽度学习系统(BLS)是最近提出的一种准确而高效的新兴机器学习算法,已在分类、回归等问题中展现出优越的学习性能.然而,传统BLS以最小二乘作为学习准则,易于受到离群值的干扰从而生成不准确的学习模型.针对该问题,提出一种基于M-estimator的鲁棒宽度学习系统(RBLS).与BLS不同,RBLS在学习模型中使用具有鲁棒特性的M-estimator代价函数来替代传统的最小二乘代价函数,并采用拉格朗日乘子法和迭代加权最小二乘方法进行优化求解.在迭代学习过程中,正常样本和离群值样本将根据其训练误差的大小而被逆向赋予不同的权重,从而有效抑制或消除离群值残差对学习模型的不利影响.实验结果表明,作为一种统一的鲁棒学习框架,RBLS可以融合使用不同的M-estimator加权策略,并能够取得较对比算法更好的泛化性能和鲁棒性.

    Abstract:

    Broad learning system (BLS) is an accurate and efficient machine learning algorithm proposed recently, which has shown excellent performance in classification, regression and other problems. However, the traditional BLS takes least squares as learning criterion, which is prone to be affected by outliers and thus generates inaccurate learning models. To solve this problem, this paper proposes a robust broad learning system (RBLS) based on M-estimator. Different from BLS, the RBLS uses a robust M-estimator cost function to replace the traditional least squares cost function in the learning model, and adopts the Lagrange multiplier method and the iteratively reweighted least squares method to seek for an optimal solution. In the iterative learning process, the normal sample and the outlier sample will be reversely assigned different weights according to the size of their training errors, so as to effectively suppress or eliminate the adverse effects of the outlier residual on the learning model. Experimental results show that, as a unified robust learning framework, RBLS can combine different M-estimator weighting strategies and achieve better generalization performance and robustness than the comparison algorithms.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-08-23
  • 最后修改日期:2022-01-13
  • 录用日期:2022-01-28
  • 在线发布日期: 2022-03-01
  • 出版日期: