Priori Anchor Labels Supervised Scalable Multi-View Bipartite Graph Clustering

Although multi-view clustering (MVC) has achieved remarkable performance by integrating the complementary information of views, it is inefficient when facing scalable data. Proverbially, anchor strategy can mitigate such a challenge certain extent. However, unsupervised dynamic usually cannot obtain optimal anchors for MVC. The main reasons are that does not consider fairness different views and lacks priori supervised guidance. To completely solve these problems, we first propose graph regularization (PAGG) bipartite clustering, dubbed as SMGC method. Specifically, learns few representative consensus to simulate numerous view data well, constructs bridge affinities between original points. In order largely improve quality anchors, PAGG predefines prior labels constrain with discriminative cluster structure fair allocation, better be obtained fast clustering. Experimentally, abundant experiments accomplished on six benchmark datasets, experimental results fully demonstrate effectiveness efficiency our SMGC.