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

Despite the empirical success and practical significance of (relational)
knowledge distillation that matches (the relations of) features between teacher
and student models, the corresponding theoretical interpretations remain
limited for various knowledge distillation paradigms. In this work, we take an
initial step toward a theoretical understanding of relational knowledge
distillation (RKD), with a focus on semi-supervised classification problems. We
start by casting RKD as spectral clustering on a population-induced graph
unveiled by a teacher model. Via a notion of clustering error that quantifies
the discrepancy between the predicted and ground truth clusterings, we
illustrate that RKD over the population provably leads to low clustering error.
Moreover, we provide a sample complexity bound for RKD with limited unlabeled
samples. For semi-supervised learning, we further demonstrate the label
efficiency of RKD through a general framework of cluster-aware semi-supervised
learning that assumes low clustering errors. Finally, by unifying data
augmentation consistency regularization into this cluster-aware framework, we
show that despite the common effect of learning accurate clusterings, RKD
facilitates a "global" perspective through spectral clustering, whereas
consistency regularization focuses on a "local" perspective via expansion.

关于关系性知识蒸馏的理论解释和在半监督分类问题中的应用，本文通过将关系性知识蒸馏转化为基于教师模型的谱聚类，并量化聚类误差来证明其有效性，并通过示例复杂度界限和群集感知的半监督学习框架进一步展示了其高标记效率。此外，通过将数据增强一致性正则化与群集感知框架统一，发现关系性知识蒸馏通过谱聚类提供了 “全局” 视角，而一致性正则化则聚焦于 “局部” 视角的扩展。