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Label-noise robust classification with multi-view learning

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Abstract

Label noise is often contained in the training data due to various human factors or measurement errors, which significantly causes a negative effect on classifiers. Despite many previous methods that have been proposed to learn robust classifiers, they are mainly based on the single-view feature. On the other hand, although existing multi-view classification methods benefit from the more comprehensive information, they rarely consider label noise. In this paper, we propose a novel label-noise robust classification model with multi-view learning to overcome these limitations. In the proposed model, not only the classifier learning but also the label-noise removal can benefit from the multi-view information. Specifically, we relax the label matrix of the basic multi-view least squares regression model, and develop a nonlinear transformation with a natural probabilistic approximation in the process of labels, which is conveniently optimized and beneficial to improve the discriminative ability of classifiers. Moreover, we preserve the intrinsic manifold structure of multi-view data on the relaxed label matrix, facilitating the process of label relaxation. For optimizing the proposed model with the nonlinear transformation, we derive a lemma about the partial derivation of the softmax related function, and develop an efficient alternating algorithm. Experimental evaluations on six real-world datasets confirm the advantages of the proposed method, compared to the related state-of-the-art methods.

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Authors and Affiliations

  1. Guangdong Key Laboratory of IoT Information Technology, School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    NaiYao Liang, ZuYuan Yang, LingJiang Li, ZhenNi Li & ShengLi Xie

  2. Key Laboratory of iDetection and Manufacturing-IoT, Ministry of Education, Guangzhou, 510006, China

    LingJiang Li

  3. Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, 510006, China

    ZhenNi Li

Authors
  1. NaiYao Liang
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  2. ZuYuan Yang
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  3. LingJiang Li
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  4. ZhenNi Li
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  5. ShengLi Xie
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Corresponding author

Correspondence to ZuYuan Yang.

Additional information

This work was supported by the Key-Area Research and Development Program of Guangdong Province (Grant No. 2019B010154002), the Guangdong Natural Science Foundation (Grant No. 2022A1515010688), and the National Natural Science Foundation of China (Grant No. 61722304).

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Liang, N., Yang, Z., Li, L. et al. Label-noise robust classification with multi-view learning. Sci. China Technol. Sci. 66, 1841–1854 (2023). https://doi.org/10.1007/s11431-021-2139-0

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