Unpaired Multi-View Graph Clustering with Cross-View Structure Matching
This addresses a practical issue in real-world multi-view clustering applications where data correspondences are often missing, offering a unified solution that improves existing methods for unpaired scenarios.
The paper tackles the data-unpaired problem in multi-view clustering, where sample correspondences between views are incomplete, by proposing a parameter-free graph clustering framework that uses structural information to refine cross-view correspondences, achieving effective performance on both fully and partially unpaired datasets.
Multi-view clustering (MVC), which effectively fuses information from multiple views for better performance, has received increasing attention. Most existing MVC methods assume that multi-view data are fully paired, which means that the mappings of all corresponding samples between views are pre-defined or given in advance. However, the data correspondence is often incomplete in real-world applications due to data corruption or sensor differences, referred as the data-unpaired problem (DUP) in multi-view literature. Although several attempts have been made to address the DUP issue, they suffer from the following drawbacks: 1) Most methods focus on the feature representation while ignoring the structural information of multi-view data, which is essential for clustering tasks; 2) Existing methods for partially unpaired problems rely on pre-given cross-view alignment information, resulting in their inability to handle fully unpaired problems; 3) Their inevitable parameters degrade the efficiency and applicability of the models. To tackle these issues, we propose a novel parameter-free graph clustering framework termed Unpaired Multi-view Graph Clustering framework with Cross-View Structure Matching (UPMGC-SM). Specifically, unlike the existing methods, UPMGC-SM effectively utilizes the structural information from each view to refine cross-view correspondences. Besides, our UPMGC-SM is a unified framework for both the fully and partially unpaired multi-view graph clustering. Moreover, existing graph clustering methods can adopt our UPMGC-SM to enhance their ability for unpaired scenarios. Extensive experiments demonstrate the effectiveness and generalization of our proposed framework for both paired and unpaired datasets.