Multi-level Reliable Guidance for Unpaired Multi-view Clustering

TL;DR

This paper introduces MRG-UMC, a novel multi-level guidance method for unpaired multi-view clustering that improves clustering confidence and consistency without relying on paired samples, outperforming existing methods.

Contribution

The paper proposes a new multi-level reliable guidance framework for unpaired multi-view clustering, integrating inner-view, synthesized-view, and cross-view strategies to enhance clustering confidence.

Findings

Achieves an average NMI improvement of 12.95% over state-of-the-art methods.

Effectively reduces boundary sample impact through high-confidence sample pairs.

Outperforms existing UMC methods on multiple datasets.

Abstract

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.