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.

翻译：对不完整多视角数据进行分类是不可避免的，因为在实际应用中广泛存在任意视角缺失的情况。尽管已取得巨大进展，但由于缺失视角具有相对较高的不确定性，现有的不完整多视角方法仍难以获得可信的预测。首先，缺失视角具有高不确定性，因此提供单一确定性插补是不合理的。其次，插补数据本身的质量也存在高不确定性。为了探索和利用这种不确定性，我们提出了一种不确定性诱导的不完整多视角数据分类（UIMC）模型，在稳定可靠的框架下对不完整多视角数据进行分类。我们通过构建分布并进行多次采样来表征缺失视角的不确定性，并根据采样质量自适应地利用它们。相应地，所提出的方法实现了更可感知的插补和可控的融合。具体而言，我们以可用视角为条件，为每个缺失数据建模一个分布，从而引入不确定性。然后采用基于证据的融合策略，确保插补视角的可信集成。在多个基准数据集上进行了大量实验，我们的方法在性能和可信度方面均达到了最先进水平。