Trusted Multi-view Learning with Label Noise

arXiv:2404.11944v1 Announce Type: new
Abstract: 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 …

abstract accuracy applications arxiv class classification cs.lg decision distribution enabling focus however improving instance issue learn noise researchers safety safety-critical type uncertainty view