Trusted Multi-view Learning with Label Noise

• URL: http://arxiv.org/abs/2404.11944v2
• Date: Fri, 10 May 2024 06:20:22 GMT
• Title: Trusted Multi-view Learning with Label Noise
• Authors: Cai Xu, Yilin Zhang, Ziyu Guan, Wei Zhao,
• Abstract summary: Multi-view learning methods often focus on improving decision accuracy while neglecting the decision uncertainty. We propose a trusted multi-view noise refining method to solve this problem. We empirically compare TMNR with state-of-the-art trusted multi-view learning and label noise learning baselines on 5 publicly available datasets.
• Score: 17.458306450909316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view learning methods often focus on improving decision accuracy while neglecting the decision uncertainty, which significantly restricts their applications in safety-critical applications. To address this issue, researchers propose trusted multi-view methods that learn the class distribution for each instance, enabling the estimation of classification probabilities and uncertainty. However, these methods heavily rely on high-quality ground-truth labels. This motivates us to delve into a new generalized trusted multi-view learning problem: how to develop a reliable multi-view learning model under the guidance of noisy labels? We propose a trusted multi-view noise refining method to solve this problem. We first construct view-opinions using evidential deep neural networks, which consist of belief mass vectors and uncertainty estimates. Subsequently, we design view-specific noise correlation matrices that transform the original opinions into noisy opinions aligned with the noisy labels. Considering label noises originating from low-quality data features and easily-confused classes, we ensure that the diagonal elements of these matrices are inversely proportional to the uncertainty, while incorporating class relations into the off-diagonal elements. Finally, we aggregate the noisy opinions and employ a generalized maximum likelihood loss on the aggregated opinion for model training, guided by the noisy labels. We empirically compare TMNR with state-of-the-art trusted multi-view learning and label noise learning baselines on 5 publicly available datasets. Experiment results show that TMNR outperforms baseline methods on accuracy, reliability and robustness. The code and appendix are released at https://github.com/YilinZhang107/TMNR.

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