Trusted Open-World Multi-View Classification with Dynamic Opinion Aggregation

Multi-view classification has demonstrated its ability to integrate diverse sources of information to significantly boost classification accuracy. To further enhance the reliability of these results, trusted multi-view learning methods have been developed. However, these approaches are designed for closed-set scenarios and fail when novel or unknown categories appear in open-world contexts. To address this limitation, we introduce the concept of Open Multi-View Learning, with the objective of detecting unknown categories with low confidence scores. We propose Trusted Open-World Multi-View Classification method for this problem. Specifically, we employ subjective logic to measure the uncertainty of data views. On these grounds, we propose dynamic opinion aggregation strategy based on their uncertainty measurements and theoretically prove this strategy can effectively detect unknown multi-view categories. The inter-view opinion consistency regularization is also adopted to mitigate conflicts between views. Experiments conducted on various multi-view datasets validate the reliability and robustness of our method.