  Existing multi-view classification algorithms focus on promoting accuracy by
exploiting different views, typically integrating them into common
representations for follow-up tasks. Although effective, it is also crucial to
ensure the reliability of both the multi-view integration and the final
decision, especially for noisy, corrupted and out-of-distribution data.
Dynamically assessing the trustworthiness of each view for different samples
could provide reliable integration. This can be achieved through uncertainty
estimation. With this in mind, we propose a novel multi-view classification
algorithm, termed trusted multi-view classification (TMC), providing a new
paradigm for multi-view learning by dynamically integrating different views at
an evidence level. The proposed TMC can promote classification reliability by
considering evidence from each view. Specifically, we introduce the variational
Dirichlet to characterize the distribution of the class probabilities,
parameterized with evidence from different views and integrated with the
Dempster-Shafer theory. The unified learning framework induces accurate
uncertainty and accordingly endows the model with both reliability and
robustness against possible noise or corruption. Both theoretical and
experimental results validate the effectiveness of the proposed model in
accuracy, robustness and trustworthiness.
