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.

翻译：多视角聚类能够以无监督方式学习一致性表示，将数据样本划分至对应类别，近年来受到越来越多的关注。然而，现有大多数深度聚类方法通过视角级聚合方式从多个视角学习一致性表示或视角特定表示，忽视了所有样本间的结构关系。本文针对这些问题，提出了一种新颖的多视角聚类网络，称为全局与跨视角特征聚合多视角聚类(GCFAggMVC)。具体而言，该方法通过跨样本与跨视角特征聚合获取来自多个视角的一致性数据表示，充分探索相似样本的互补性。此外，我们利用结构引导的对比学习模块对齐一致性表示与视角特定表示，使来自不同样本的高结构关系视角特定表示趋于相似。该模块作为一种灵活的多视角数据表示模块，可嵌入至其他框架中，应用于不完整多视角数据聚类任务。大量实验表明，所提方法在完整多视角数据聚类任务与不完整多视角数据聚类任务中均取得了优异性能。