Center consistency guided multi-view embedding anchor learning for large-scale graph clustering

Abstract

Multi-view clustering has attracted extensive attention since it can integrate the complementary information of different views. Nonetheless, most existing methods suffer from cubic time computational complexity, so they have to face the challenge of maladjustment to medium and large-scale datasets. Whereupon, researchers usually employ anchor selection strategies to select a proportion of data points as landmarks to simulate the original data, and then build a bipartite graph to bridge the relationship between the original data points and anchors. However, for multi-view data, the existing strategies have three shortcomings, including: (1) on account of redundancy and corruption, the quality of anchors may be greatly affected; (2) these strategies are static ones or deficiency of views consistency; and (3) due to the differences in source, latent distribution, and the dimension of each view, the consensus bipartite graph cannot exploit the comprehensive information of all views precisely. In order to address these problems, this paper first proposes a novel method for scalable clustering, dubbed center consistency guided multi-view embedding anchor learning (2C-MEAL). Specifically, considering the redundancy and corruption, embedding learning is adopted to filter out the adverse information, as well as unify the dimensions of all views. Then, dynamic center consistency guided anchor learning is proposed to adaptively learn a set of high-quality anchors in the clean embedding space. Meanwhile, the refined bipartite graph is obtained for clustering purposes. Overall, 2C-MEAL has promising clustering performance and linear complexity. Experimentally, clustering experiments on benchmark datasets demonstrate that 2C-MEAL possesses effectiveness and efficiency over state-of-the-art methods.