GCFAgg: Global and Cross-view Feature Aggregation for Multi-view
  Clustering
Weiqing Yan Yuanyang Zhang Chenlei Lv Chang Tang Guanghui Yue Liang Liao Weisi Lin
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.