Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature Aggregation for Multi-View Clustering (GCFAggMVC). Specifically, the consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary ofsimilar samples. Moreover, we align the consensus representation and the view-specific representation by the structure-guided contrastive learning module, which makes the view-specific representations from different samples with high structure relationship similar. The proposed module is a flexible multi-view data representation module, which can be also embedded to the incomplete multi-view data clustering task via plugging our module into other frameworks. Extensive experiments show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks.

本文提出了一种新型的多视角聚类网络和结构导向对比学习模块，称为全局和跨视角特征聚合多视角聚类（GCFAggMVC），通过跨样本和跨视野特征聚合获得来自多视角的共识数据表达式，并通过结构引导对比学习模块将共识表达式和视角特定表达式对齐，从而实现对不同样本的视角特定表达式进行对比，从而对完整和不完整的多视角数据聚类任务均获得了良好的性能。

GCFAgg: 多视角聚类的全局和跨视图特征聚合