  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.
