Incomplete multi-view clustering has become one of the important research problems due to the extensive missing multi-view data in the real world. Although the existing methods have made great progress, there are still some problems: 1) most methods cannot effectively mine the information hidden in the missing data; 2) most methods typically divide representation learning and clustering into two separate stages, but this may affect the clustering performance as the clustering results directly depend on the learned representation. To address these problems, we propose a novel incomplete multi-view clustering method with hierarchical information transfer. Firstly, we design the view-specific Graph Convolutional Networks (GCN) to obtain the representation encoding the graph structure, which is then fused into the consensus representation. Secondly, considering that one layer of GCN transfers one-order neighbor node information, the global graph propagation with the consensus representation is proposed to handle the missing data and learn deep representation. Finally, we design a weight-sharing pseudo-classifier with contrastive learning to obtain an end-to-end framework that combines view-specific representation learning, global graph propagation with hierarchical information transfer, and contrastive clustering for joint optimization. Extensive experiments conducted on several commonly-used datasets demonstrate the effectiveness and superiority of our method in comparison with other state-of-the-art approaches. The code is available at https://github.com/KelvinXuu/GHICMC.

翻译：不完整多视图聚类因现实世界中广泛存在的多视图数据缺失问题而成为重要的研究方向。尽管现有方法已取得显著进展，但仍存在以下问题：1）大多数方法无法有效挖掘缺失数据中隐藏的信息；2）多数方法通常将表示学习与聚类划分为两个独立阶段，但这可能影响聚类性能，因为聚类结果直接依赖于学习到的表示。为解决这些问题，本文提出一种具有层次信息传递机制的新型不完整多视图聚类方法。首先，我们设计视图特定的图卷积网络（GCN）来获取编码图结构的表示，并将其融合为共识表示。其次，考虑到单层GCN仅传递一阶邻接节点信息，我们提出基于共识表示的全局图传播机制来处理缺失数据并学习深层表示。最后，我们设计具有对比学习的权重共享伪分类器，构建端到端框架，将视图特定表示学习、基于层次信息传递的全局图传播以及对比聚类进行联合优化。在多个常用数据集上的大量实验表明，相较于其他先进方法，本方法具有显著的有效性和优越性。代码已发布于https://github.com/KelvinXuu/GHICMC。