Auto-weighted graph tensor and rank-constrained bipartite graph fusion for multi-view clustering

Tensor multi-view clustering generally outperforms non-tensor counterparts, as the tensor structure can effectively capture the higher-order correlations of data. Although the t-SVD-based tensor nuclear norm has shown remarkable performance, it treats the similar information across all views equally, overlooking the higher-order similarities between similar graphs. To address this issue, we propose a Pearson Correlation Coefficient-based $\text{A}$uto-weighted $\text{G}$raph $\text{T}$ensor and $\text{R}$ank-constrained $\text{B}$ipartite $\text{G}$raph $\text{F}$usion (AGTRBGF) approach for multi-view clustering. Specifically, the P-AGT learning method breaks free from the constraints of predefined weights, automatically assigning optimal weight values for each similarity graph by leveraging the higher-order similarities among the similar graphs of different views. Additionally, the Laplace rank is utilized to constrain the adaptive graph fusion, endowing learned consensus graph with strong diagonal structure and enhancing the model’s robustness. Experiments conducted on distinct datasets validate the effectiveness and superior clustering performance of AGTRBGF.