Embracing Aleatoric and Epistemic Uncertainty for Trusted Open-world Multi-view Classification

Multi-view classification fuses heterogeneous information to achieve more comprehensive data descriptions. To deliver trustworthy decisions, trusted multi-view classification (TMVC) has emerged as a crucial research paradigm. However, existing TMVC methods assume closed-set environments and inevitably fail when novel categories emerge in open-world settings. This stems from two key limitations: they neglect data-inherent aleatoric uncertainty, leaving cross-view alignment vulnerable to noise; and their fusion strategies lack epistemic coherence, producing overconfident and unreliable predictions that are catastrophically amplified for unknown samples. To address these, we propose a novel framework jointly Embracing aleatoric and epistemic Uncertainty for trusted Open-world multi-view classification (EU${}^2$O). Specifically, we quantify view-specific aleatoric uncertainty and align cross-view representations to obtain a robust joint representation, which serves as a reliable reference for subsequent opinion aggregation. Besides, we propose an Uncertainty-Driven Proportional Conflict Redistribution (UD-PCR) operator that aggregates multi-view subjective opinions by adaptively redistributing conflict mass in proportion to calibrates belief masses and uncertainty. We also theoretically prove its effectiveness, particularly in open-world scenarios. A novel Jousselme consistency loss further enforce cross-view epistemic alignment at a linear complexity. Experiments on multiple benchmarks demonstrate superior closed-set accuracy and open-world rejection.