Multi-view Clustering via Bi-level Decoupling and Consistency Learning

• URL: http://arxiv.org/abs/2508.13499v1
• Date: Tue, 19 Aug 2025 04:17:54 GMT
• Title: Multi-view Clustering via Bi-level Decoupling and Consistency Learning
• Authors: Shihao Dong, Yuhui Zheng, Huiying Xu, Xinzhong Zhu,
• Abstract summary: Multi-view clustering has shown to be an effective method for analyzing underlying patterns in multi-view data.<n>We propose a novel Bi-level Decoupling and Consistency Learning framework to explore the effective representation for multi-view data.
• Score: 16.5206977323695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-view clustering has shown to be an effective method for analyzing underlying patterns in multi-view data. The performance of clustering can be improved by learning the consistency and complementarity between multi-view features, however, cluster-oriented representation learning is often overlooked. In this paper, we propose a novel Bi-level Decoupling and Consistency Learning framework (BDCL) to further explore the effective representation for multi-view data to enhance inter-cluster discriminability and intra-cluster compactness of features in multi-view clustering. Our framework comprises three modules: 1) The multi-view instance learning module aligns the consistent information while preserving the private features between views through reconstruction autoencoder and contrastive learning. 2) The bi-level decoupling of features and clusters enhances the discriminability of feature space and cluster space. 3) The consistency learning module treats the different views of the sample and their neighbors as positive pairs, learns the consistency of their clustering assignments, and further compresses the intra-cluster space. Experimental results on five benchmark datasets demonstrate the superiority of the proposed method compared with the SOTA methods. Our code is published on https://github.com/LouisDong95/BDCL.

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