Learning latent disentangled embeddings and graphs for multi-view clustering

• Published in: Pattern recognition 2024-12, Vol.156, p.110839, Article 110839
• Main Authors: Zhang, Chao, Chen, Haoxing, Li, Huaxiong, Chen, Chunlin
• Format: Article
• Language: English
• Subjects: Embedding disentanglement; Graph learning; Low-rank tensor; Multi-view clustering
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2024.110839

Graph based methods have recently attracted much attention for multi-view clustering. Most existing methods seek the latent shared embeddings to learn a unified similarity graph or fuse multiple view-specific graphs to a consensus one for clustering, which may not sufficiently explore the common and complementary information among views. Besides, the high-order inter-view correlations are not fully investigated. To address these issues, this paper proposes a latent Disentangled Embeddings and GRaphs based multi-viEw clustEring (DEGREE) method, which considers the common and view-specific information in a latent subspace by explicit embedding disentanglement and multiple graphs learning. We assume that each view can be generated from a shared latent embedding and a corresponding view-specific embedding, which model the common information and exclusive complementary information among views, respectively. The intra-view and inter-view exclusivities among embeddings are encouraged by an orthogonality regularizer. To fully use the underlying information, we excavate the pairwise instance relations in both shared embedding and diverse view-specific embeddings by learning multiple graphs. Besides, a tensor singular value decomposition (t-SVD) based tensor nuclear norm regularizer is imposed on view-specific graphs, which helps to explore the high-order inter-view correlations. An alternative optimization algorithm is designed to solve the proposed model. Experimental evaluations on several popular datasets demonstrate that our DEGREE method outperforms the state-of-the-art methods. •Our method exploits the multi-view information by latent embedding disentanglement.•Both intra-view and inter-view exclusivity are considered for disentanglement.•The high-order correlations are captured with low-rank tensor regularization.•Experimental results show the effectiveness of the method.