GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering. (arXiv:2305.06799v1 [cs.CV])

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 …

aggregation arxiv attention clustering consensus data feature global learn multiple representation unsupervised