Coupled double consensus multi-graph fusion for multi-view clustering.

Multi-view graph clustering (MVGC) is a technique that combines information from multiple views to perform clustering analysis on graph data. However, the consensus information between the different views is not fully utilized. Additionally, the influence of noise is inevitable, leading to insufficient robustness of the algorithm. To address these issues, this paper proposes coupled double consensus multi-graph fusion for multi-view clustering method (CDCMGF). Specifically, we first utilize the self-expressive property of the original data to obtain similarity graphs. Next, we further integrate the fusion of multiple similarity graphs into a consensus graph. However, the consensus information from different views is still not fully utilized, and there is some noise. Then, we utilize the self-expressive property of the consensus graph to obtain a much cleaner consensus graph. Fourth, we stack the two consensus graphs into a tensor, which is subjected to the constraint of the tensor nuclear norm (TNN). Then, the two consensus graphs reinforce each other, allowing for the comprehensive utilization of the consensus information from different views and reducing the influence of noise. Ultimately, by utilizing the augmented Lagrange multiplier method (ALM), the four steps outlined above are unified into a framework. The CDCMGF achieves a performance improvement of up to 64.86%, and the experimental results from various public datasets indicate that the CDCMGF algorithm outperforms the state-of-the-art algorithms. In other words, these experimental results validate the importance of fully utilizing the consensus information among the different views. The code is publicly available at https://github.com/TongWuahpu/CDCMGF. [ABSTRACT FROM AUTHOR]