Learning a Label-Noise Robust Logistic Regression: Analysis and Experiments

被引:0
|
作者
Bootkrajang, Jakramate [1 ]
Kaban, Ata [1 ]
机构
[1] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England
关键词
label noise; logistic regression; robust learning; gradient ascent optimisation; generalisation error bounds; DISCRIMINANT-ANALYSIS; INITIAL SAMPLES; MICROARRAYS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Label-noise robust logistic regression (rLR) is an extension of logistic regression that includes a model of random mislabelling. This paper attempts a theoretical analysis of rLR. By decomposing and interpreting the gradient of the likelihood objective of rLR as employed in gradient ascent optimisation, we get insights into the ability of the rLR learning algorithm to counteract the negative effect of mislabelling as a result of an intrinsic re-weighting mechanism. We also give an upper-bound on the error of rLR using Rademacher complexities.
引用
收藏
页码:569 / 576
页数:8
相关论文
共 50 条
  • [1] Label-noise robust classification with multi-view learning
    LIANG NaiYao
    YANG ZuYuan
    LI LingJiang
    LI ZhenNi
    XIE ShengLi
    [J]. Science China(Technological Sciences)., 2023, 66 (06) - 1854
  • [2] Label-noise robust classification with multi-view learning
    NaiYao Liang
    ZuYuan Yang
    LingJiang Li
    ZhenNi Li
    ShengLi Xie
    [J]. Science China Technological Sciences, 2023, 66 : 1841 - 1854
  • [3] BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning
    Zhang, Jingfeng
    Song, Bo
    Wang, Haohan
    Han, Bo
    Liu, Tongliang
    Liu, Lei
    Sugiyama, Masashi
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2024, 46 (06) : 4398 - 4409
  • [4] Label-noise robust classification with multi-view learning
    Liang, NaiYao
    Yang, ZuYuan
    Li, LingJiang
    Li, ZhenNi
    Xie, ShengLi
    [J]. SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2023, 66 (06) : 1841 - 1854
  • [5] Label-noise robust classification with multi-view learning
    LIANG NaiYao
    YANG ZuYuan
    LI LingJiang
    LI ZhenNi
    XIE ShengLi
    [J]. Science China Technological Sciences, 2023, (06) : 1841 - 1854
  • [6] Label-Noise Robust Generative Adversarial Networks
    Kaneko, Takuhiro
    Ushiku, Yoshitaka
    Harada, Tatsuya
    [J]. 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, : 2462 - 2471
  • [7] A survey of label-noise deep learning for medical image analysis
    Shi, Jialin
    Zhang, Kailai
    Guo, Chenyi
    Yang, Youquan
    Xu, Yali
    Wu, Ji
    [J]. MEDICAL IMAGE ANALYSIS, 2024, 95
  • [8] Label-Noise Robust Deep Generative Model for Semi-Supervised Learning
    Yoon, Heegeon
    Kim, Heeyoung
    [J]. TECHNOMETRICS, 2023, 65 (01) : 83 - 95
  • [9] Label-Noise Resistant Logistic Regression for Functional Data Classification with an Application to Alzheimer's Disease Study
    Lee, Seokho
    Shin, Hyejin
    Lee, Sang Han
    [J]. BIOMETRICS, 2016, 72 (04) : 1325 - 1335
  • [10] Learning kernel logistic regression in the presence of class label noise
    Bootkrajang, Jakramate
    Kahan, Ata
    [J]. PATTERN RECOGNITION, 2014, 47 (11) : 3641 - 3655