Multi-View Clustering Through Hypergraphs Integration on Stiefel Manifold

Multi-graph clustering aims at integrating complementary information across multiple graphs to partition multi-view data into underlying clusters. Most current methods rely on pairwise graphs to characterize each view and then employ popular Euclidean averaging to integrate multiple graphs. How-ever, operations of the pairwise graphs on Euclidean space result in insufficient robustness to noise. To address the issue, we propose a method called multi-hypergraph clustering on the Stiefel manifold. First, a hypergraph for each view is constructed to extract high-order relations, which are more resistant to the noise than pairwise graphs. Second, a consensus partition matrix is derived through integrating the multiple hypergraphs on the Stiefel manifold. Such integration is completely driven by the manifold-based operation and enables an effective fusion to mitigate noise contamination, thus improving multi-view clustering performance. Empirical evaluations on five benchmark datasets have demonstrated that our method achieves consistent performance improvement compared with six baseline methods.