Adaptive graph fusion learning for multi-view spectral clustering

Multi-view data suffer from issues related to low quality and heterogeneity, which leads to instability issues in existing learning models for clustering. To overcome the limitations of traditional clustering methods, we propose a novel multi-view spectral clustering based on a contrastive feedback strategy. The proposed method constructs kernelized graphs to obtain higher-order feature representations, mapping primary sample data from different views into an isomorphic feature space and masking differences in type and structure between multiple views. In the meantime, the proposed method leverages a contrastive feedback optimization strategy based on mutual information to iteratively optimize the kernelized graphs of all views and their dynamically fused graph. This method effectively exploits the consistency and complementary information between multi-view, further improving the clustering accuracy. Experiments on multiple datasets demonstrate that the proposed method achieves satisfactory results in terms of clustering, validating the feasibility and effectiveness of the contrastive feedback strategy.