In this paper, we address the challenging problem of unpaired multi-view
clustering (UMC), aiming to perform effective joint clustering using unpaired
observed samples across multiple views. Commonly, traditional incomplete
multi-view clustering (IMC) methods often depend on paired samples to capture
complementary information between views. However, the strategy becomes
impractical in UMC due to the absence of paired samples. Although some
researchers have attempted to tackle the issue by preserving consistent cluster
structures across views, they frequently neglect the confidence of these
cluster structures, especially for boundary samples and uncertain cluster
structures during the initial training. Therefore, we propose a method called
Multi-level Reliable Guidance for UMC (MRG-UMC), which leverages multi-level
clustering to aid in learning a trustworthy cluster structure across
inner-view, cross-view, and common-view, respectively. Specifically, within
each view, multi-level clustering fosters a trustworthy cluster structure
across different levels and reduces clustering error. In cross-view learning,
reliable view guidance enhances the confidence of the cluster structures in
other views. Similarly, within the multi-level framework, the incorporation of
a common view aids in aligning different views, thereby reducing the clustering
error and uncertainty of cluster structure. Finally, as evidenced by extensive
experiments, our method for UMC demonstrates significant efficiency
improvements compared to 20 state-of-the-art methods.

我们提出了一种名为 MRG-UMC 的方法，该方法利用多级聚类来学习跨视图、内视图和公共视图的可靠聚类结构，减少聚类错误和不确定性。

无配对多视角聚类的多层可靠引导

Multi-level Reliable Guidance for Unpaired Multi-view Clustering

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。