  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.
