This paper focuses on unpaired multi-view clustering (UMC), a challenging
problem where paired observed samples are unavailable across multiple views.
The goal is to perform effective joint clustering using the unpaired observed
samples in all views. In incomplete multi-view clustering, existing methods
typically rely on sample pairing between views to capture their complementary.
However, that is not applicable in the case of UMC. Hence, we aim to extract
the consistent cluster structure across views. In UMC, two challenging issues
arise: uncertain cluster structure due to lack of label and uncertain pairing
relationship due to absence of paired samples. We assume that the view with a
good cluster structure is the reliable view, which acts as a supervisor to
guide the clustering of the other views. With the guidance of reliable views, a
more certain cluster structure of these views is obtained while achieving
alignment between reliable views and other views. Then we propose Reliable view
Guidance with one reliable view (RG-UMC) and multiple reliable views (RGs-UMC)
for UMC. Specifically, we design alignment modules with one reliable view and
multiple reliable views, respectively, to adaptively guide the optimization
process. Also, we utilize the compactness module to enhance the relationship of
samples within the same cluster. Meanwhile, an orthogonal constraint is applied
to latent representation to obtain discriminate features. Extensive experiments
show that both RG-UMC and RGs-UMC outperform the best state-of-the-art method
by an average of 24.14\% and 29.42\% in NMI, respectively.

该研究专注于非配对多视图聚类问题，提出了一种可靠视图引导的方法来解决跨视图聚类中的不确定聚类结构和样本配对关系不确定的问题，并通过对齐模块和紧凑性模块进一步优化聚类效果，实验证明该方法的优越性。