  Classifying incomplete multi-view data is inevitable since arbitrary view
missing widely exists in real-world applications. Although great progress has
been achieved, existing incomplete multi-view methods are still difficult to
obtain a trustworthy prediction due to the relatively high uncertainty nature
of missing views. First, the missing view is of high uncertainty, and thus it
is not reasonable to provide a single deterministic imputation. Second, the
quality of the imputed data itself is of high uncertainty. To explore and
exploit the uncertainty, we propose an Uncertainty-induced Incomplete
Multi-View Data Classification (UIMC) model to classify the incomplete
multi-view data under a stable and reliable framework. We construct a
distribution and sample multiple times to characterize the uncertainty of
missing views, and adaptively utilize them according to the sampling quality.
Accordingly, the proposed method realizes more perceivable imputation and
controllable fusion. Specifically, we model each missing data with a
distribution conditioning on the available views and thus introducing
uncertainty. Then an evidence-based fusion strategy is employed to guarantee
the trustworthy integration of the imputed views. Extensive experiments are
conducted on multiple benchmark data sets and our method establishes a
state-of-the-art performance in terms of both performance and trustworthiness.
