Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization

Incomplete multi-view clustering (IMC) has achieved widespread attention due to its advantage in fusing the multi-view information when the view samples are unobserved partly. Recently, it is shown that the clustering performance in the subspace can be improved by preserving the clustering structure of each view, but the problem of the inconsistent clustering structure caused by the incomplete graphs are seldom considered, restricting the clustering performance. Motivated by the clustering interpretation of the orthogonal non-negative matrix factorization, it is employed to unify the clustering structure of the data, and a new model called Incomplete Graph-regularized Orthogonal Non-negative Matrix Factorization (IGONMF) is proposed in this paper. In IGONMF, the reproduced representation is developed, based on which, a set of incomplete graphs are utilized to fully take advantage of the geometric structure of the data. And the orthogonality is further employed to alleviate the problem of the inconsistent clustering structure. Also, we design an effective iterative updating algorithm to solve the proposed model, along with its analysis on the convergence and the computational cost. Finally, experimental results on several real-world datasets indicate that our method is superior to the related state-of-the-art methods.