Multi-level Reliable Guidance for Unpaired Multi-view Clustering

• URL: http://arxiv.org/abs/2407.01247v2
• Date: Tue, 2 Jul 2024 09:55:26 GMT
• Title: Multi-level Reliable Guidance for Unpaired Multi-view Clustering
• Authors: Like Xin, Wanqi Yang, Lei Wang, Ming Yang,
• Abstract summary: We propose a method called Multi-level Reliable Guidance for UMC (MRG-UMC) MRG-UMC leverages multi-level clustering to aid in learning a trustworthy cluster structure across inner-view, cross-view, and common-view. In cross-view learning, reliable view guidance enhances the confidence of the cluster structures in other views.
• Score: 7.441454668534061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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