Multilevel Reliable Guidance for Unpaired Multiview Clustering

2024-07-01

IEEE Transactions on Neural Networks and Learning Systems

AI Summary

Unpaired multi-view clustering (UMC) suffers from difficulties in modeling cross-view consistency due to the absence of sample-level alignment across views. Method: This paper proposes a tri-module co-optimization framework comprising (i) intra-view multi-level spectral clustering to uncover hierarchical cluster structures, (ii) synthetic view alignment to enhance cross-view comparability, and (iii) confidence-weighted reliable guidance for active cross-view calibration. Contribution/Results: We introduce, for the first time, a multi-level reliable guidance paradigm that jointly integrates high-confidence sample-pair mining, synthetic-view-based cross-view calibration, and reliable view selection—significantly improving robustness under low-confidence cluster structures. Extensive experiments on multiple benchmark datasets demonstrate an average 12.95% improvement in Normalized Mutual Information (NMI), substantially outperforming state-of-the-art UMC methods.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

In this article, we address the challenging problem of unpaired multiview clustering (UMC), which aims to achieve effective joint clustering using unpaired samples observed across multiple views. Traditional incomplete multiview 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, multilevel reliable guidance for UMC (MRG-UMC), which integrates multilevel clustering and reliable view guidance to learn consistent and confident cluster structures from three perspectives. Specifically, inner view multilevel 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 multiview datasets. The source code is available at https://anonymous.4open.science/r/MRG-UMC-5E20

Problem

• Clustering unpaired multi-view data without sample correspondence
• Learning consistent cluster structures from low-confidence views
• Reducing cross-view discrepancies through multi-level reliable guidance

Innovation

• Inner-view multi-level clustering reduces boundary sample impact
• Synthesized-view alignment mitigates cross-view discrepancies
• Cross-view guidance enhances clustering confidence