Recently, multi-view and multi-label classification have become significant domains for comprehensive data analysis and exploration. However, incompleteness both in views and labels is still a real-world scenario for multi-view multi-label classification. In this paper, we seek to focus on double missing multi-view multi-label classification tasks and propose our dual-level contrastive learning framework to solve this issue. Different from the existing works, which couple consistent information and view-specific information in the same feature space, we decouple the two heterogeneous properties into different spaces and employ contrastive learning theory to fully disentangle the two properties. Specifically, our method first introduces a two-channel decoupling module that contains a shared representation and a view-proprietary representation to effectively extract consistency and complementarity information across all views. Second, to efficiently filter out high-quality consistent information from multi-view representations, two consistency objectives based on contrastive learning are conducted on the high-level features and the semantic labels, respectively. Extensive experiments on several widely used benchmark datasets demonstrate that the proposed method has more stable and superior classification performance.

本研究解决了多视角多标签分类中视角和标签不完整的问题，提出了一种双层对比学习框架。该方法通过两通道解耦模块有效提取一致性和互补信息，并在多个广泛使用的基准数据集上验证了该方法的分类性能显著优于现有方法。

双层对比学习框架下的不完全多视角多标签分类