Consensus Graph Filter Learning for Multiple Graph Clustering

Jiale Zou, Yan Chen, Peng Zhou, Chao Wen, Liang Du, Yuhua Qian

Abstract: Multi-view Clustering (MVC) has gained significant attention for its ability to utilize consistent and complementary information from multiple views. Graph filter-based MVC methods have recently demonstrated promising performance, attracting growing interest. However, existing graph filter-based methods typically rely on a single filter for each view. These filters are usually derived from either a specific view or a consensus graph across all views, which limits their effectiveness in fully integrating multi-view information. To address this limitation, we propose a novel method, Consensus Graph Filter Learning for Multiple Graph Clustering (CGFMVC). Unlike existing methods that rely on a single filter per view, we construct multiple graph filters by integrating information from all views. For each view, we generate intra-view graph filters to intermediate high-order information and bridge local-global structures. This approach facilitates neighborhood smoothing and preserves local consistency within each view. By learning a consensus graph filter from these multiple filters, we effectively capture global complementary information across views while preserving local consistency. CGFMVC demonstrates excellent efficiency and effectiveness on various benchmark datasets, outperforming state-of-the-art methods. The code is publicly available at https://github.com/Sean-zjl/CGFMVC.