Trusted Multi-view Learning with Noisy Labels: Mitigating the Impact of Inaccurate Guidance

A reliable multi-view learning model can be developed under the guidance of noisy labels by leveraging multi-view consistent information for detecting and refining noisy labels, and assigning higher decision uncertainty to instances belonging to easily mislabelled classes.