In this thesis, we address the challenging problem of unpaired multi-view clustering (UMC), which aims to achieve effective joint clustering using unpaired samples observed across multiple views. Traditional incomplete multi-view clustering (IMC) methods typically rely on paired samples to capture complementary information between views. However, such strategies become impractical in the UMC due to the absence of paired samples. Although some researchers have attempted to address this issue by preserving consistent cluster structures across views, effectively mining such consistency remains challenging when the cluster structures {with low confidence}. Therefore, we propose a novel method, Multi-level Reliable Guidance for UMC (MRG-UMC), which integrates multi-level clustering and reliable view guidance to learn consistent and confident cluster structures from three perspectives. Specifically, inner-view multi-level clustering exploits high-confidence sample pairs across different levels to reduce the impact of boundary samples, resulting in more confident cluster structures. Synthesized-view alignment leverages a synthesized-view to mitigate cross-view discrepancies and promote consistency. Cross-view guidance employs a reliable view guidance strategy to enhance the clustering confidence of poorly clustered views. These three modules are jointly optimized across multiple levels to achieve consistent and confident cluster structures. Furthermore, theoretical analyses verify the effectiveness of MRG-UMC in enhancing clustering confidence. Extensive experimental results show that MRG-UMC outperforms state-of-the-art UMC methods, achieving an average NMI improvement of 12.95\% on multi-view datasets. {The source code is available at: https://anonymous.4open.science/r/MRG-UMC-5E20.

翻译：本文针对无配对多视图聚类这一具有挑战性的问题展开研究，该问题旨在利用多个视图中观测到的无配对样本实现有效的联合聚类。传统的非完整多视图聚类方法通常依赖配对样本来捕捉视图间的互补信息。然而，在无配对多视图聚类场景中，由于配对样本的缺失，此类策略变得不可行。尽管已有研究者尝试通过保持视图间一致的聚类结构来解决该问题，但当聚类结构置信度较低时，有效挖掘此类一致性仍面临挑战。为此，我们提出了一种新颖的方法——多层级可靠引导的无配对多视图聚类，该方法融合了多层级聚类与可靠视图引导机制，从三个维度学习一致且高置信的聚类结构。具体而言，视图内多层级聚类利用跨不同层级的的高置信样本对来减轻边界样本的影响，从而获得置信度更高的聚类结构。合成视图对齐通过引入合成视图来弥合跨视图差异并促进一致性。跨视图引导采用可靠视图引导策略来提升聚类效果不佳视图的聚类置信度。这三个模块在多个层级上联合优化，以实现一致且高置信的聚类结构。此外，理论分析验证了MRG-UMC在提升聚类置信度方面的有效性。大量实验结果表明，MRG-UMC优于当前最先进的无配对多视图聚类方法，在多视图数据集上实现了平均12.95\%的归一化互信息提升。源代码发布于：https://anonymous.4open.science/r/MRG-UMC-5E20。