Learning Compact Semantic Information and Reliable Pseudo-Labels for Incomplete Multi-View Multi-Label Classification

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, low-redundancy 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.