  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.
