GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering
This addresses multi-view clustering for unsupervised data partitioning, but it is incremental as it builds on existing deep clustering methods by incorporating structure relationships.
The paper tackles the problem of learning consensus representations in multi-view clustering by proposing GCFAggMVC, which uses cross-sample and cross-view feature aggregation and structure-guided contrastive learning, achieving excellent performance in both complete and incomplete multi-view data clustering tasks.
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.