Multi-view data encompasses various data types, including multi-feature, multi-sequence, and multi-modal data. Multi-view multi-label classification aims to leverage the rich semantic information contained in multiple views to achieve enhanced multi-label classification performance. In practical applications, the absence of views and labels poses a significant challenge to multi-view multi-label classification tasks. Premised on the assumption that shared semantic information across multiple views is sufficient to support the downstream task, we propose CTRL, a novel incomplete multi-view multi-label classification framework to address the multi-view learning challenge on the data with partially missing views and missing labels in this paper. The core mechanism of CTRL lies in learning a high-purity, lowredundancy condensed representation that adequately captures the essential information of the original data. Specifically, we design a new objective loss to enhance the semantic information of shared cross-view within the joint representation learning process while simultaneously suppressing intra-view redundant information that is irrelevant to the downstream task. This enables CTRL to extract task-relevant representations even when views are incomplete. Furthermore, we employ the Beta Evidential Neural Network to model the label distribution. This network is then integrated with Dempster-Shafer theory, enabling our model to perform label-level classification uncertainty estimation. This also allows us to use the estimated uncertainty and belief mass to create high-reliability pseudo-labels, resulting in further gains in model performance. Experimental results on multiple benchmark datasets demonstrate the superior performance of our proposed model in terms of accuracy, robustness, and reliability.
Liu et al. (2026) studied this question.