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.

翻译：多视图聚类（MVC）通过有效融合多视图信息以提升性能，近年来受到广泛关注。现有大多数MVC方法假设多视图数据完全配对，即视图间所有对应样本的映射关系预先定义或给定。然而，实际应用中因数据损坏或传感器差异，数据对应关系往往不完整，这在多视图文献中被称为数据非配对问题（DUP）。尽管已有若干研究尝试解决DUP问题，但仍存在以下缺陷：1）多数方法侧重于特征表示，忽略了多视图数据的结构信息，而这对聚类任务至关重要；2）现有针对部分非配对问题的方法依赖于预先给定的跨视图对齐信息，导致其无法处理完全非配对问题；3）模型中不可避免的参数降低了效率与适用性。为解决这些问题，我们提出了一种新颖的无参数图聚类框架——基于跨视图结构匹配的非配对多视图图聚类框架（UPMGC-SM）。具体而言，与现有方法不同，UPMGC-SM有效利用每个视图的结构信息来优化跨视图对应关系。此外，UPMGC-SM是一个统一框架，适用于完全非配对与部分非配对的多视图图聚类。更进一步，现有图聚类方法可采纳UPMGC-SM以增强其在非配对场景下的能力。大量实验表明，所提框架在配对与非配对数据集上均具有有效性与泛化能力。