Multi-view subspace clustering with incomplete graph information

The core of multi-view clustering is how to exploit the shared and specific information of multi-view data properly. The data missing and incompleteness bring great challenges to multi-view clustering. In this paper, we propose an innovative multi-view subspace clustering method with incomplete graph information, so-called incomplete multiple graphs clustering. Specifically, we creatively separate one shared and multiple specific graphs from multiple raw graph data, and exploit the mask fusion strategy and block diagonal regulariser to obtain the inherent category information. To handle the incomplete multiple graph data, we utilise multiple indicator matrices to mark the missing elements existed in each raw graph. In addition, the weight of each raw graph is adaptively learnt according to the graph importance. The alternative direction optimization algorithm is employed to solve our proposed methods. Finally, we also analyse the algorithm convergence and the computation complexity in detail. The clustering results on six real-world datasets show that our method obviously outperforms a serious of classic incomplete multi-view clustering methods.