Trusted Multi-view Learning with Label Noise
Cai Xu Yilin Zhang Ziyu Guan Wei Zhao
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.