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. We promise to release the code and all datasets on Github and show the link here.

翻译：多视图学习方法通常侧重于提升决策准确性，却忽视了决策不确定性，这极大限制了其在安全关键型应用中的部署。为解决该问题，研究者提出了可信多视图方法，通过为每个实例学习类别分布，从而实现对分类概率与不确定性的估计。然而，这些方法高度依赖高质量的真实标签。这促使我们探索新的广义可信多视图学习问题：如何在含噪标签引导下开发可靠的多视图学习模型？我们提出了可信多视图噪声精炼方法来解决此问题。首先，利用证据深度神经网络构建视图意见（由信念质量向量与不确定性估计组成）。其次，设计视图特异性噪声关联矩阵，将原始意见转换为与含噪标签对齐的噪声意见。考虑到标签噪声源于低质量数据特征与易混淆类别，我们确保矩阵对角元素与不确定性成反比，同时将类别关联性融入非对角元素。最后，整合噪声意见并基于含噪标签，利用聚合意见上的广义最大似然损失进行模型训练。我们在5个公开数据集上将TMNR与最先进的可信多视图学习及标签噪声学习基线进行了实证比较。实验结果表明，TMNR在准确性、可靠性和鲁棒性上均优于基线方法。我们承诺将代码及所有数据集在GitHub上开源，并在此提供链接。