One Step Large-Scale Multi-view Subspace Clustering Based on Orthogonal Matrix Factorization with Consensus Graph Learning

Multi-view clustering has always been a widely concerned issue due to its wide range of applications. Since real-world datasets are usually very large, the clustering problem for large-scale multi-view datasets has always been a research hotspot. Most of the existing methods to solve the problem of large-scale multi-view data usually include several independent steps, namely anchor point generation, graph construction, and clustering result generation, which generate the inflexibility anchor points, and the process of obtaining the cluster indicating matrix and graph constructing are separating from each other, which leads to suboptimal results. Therefore, to address these issues, a one-step multi-view subspace clustering model based on orthogonal matrix factorization with consensus graph learning(CGLMVC) is proposed. Specifically, our method puts anchor point learning, graph construction, and clustering result generation into a unified learning framework, these three processes are learned adaptively to boost each other which can obtain flexible anchor representation and improve the clustering quality. In addition, there is no need for post-processing steps. This method also proposes an alternate optimization algorithm for convergence results, which is proved to have linear time complexity. Experiments on several real world large-scale multi-view datasets demonstrate its efficiency and scalability.