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Clustering with incomplete views is a challenge in multi-view clustering. Inthis paper, we provide a novel and simple method to address this issue.Specifically, the proposed method simultaneously exploits the local informationof each view and the complementary information among views to learn the commonlatent representation for all samples, which can greatly improve thecompactness and discriminability of the obtained representation. Compared withthe conventional graph embedding methods, the proposed method does notintroduce any extra regularization term and corresponding penalty parameter topreserve the local structure of data, and thus does not increase the burden ofextra parameter selection. By imposing the orthogonal constraint on the basismatrix of each view, the proposed method is able to handle the out-of-sample.Moreover, the proposed method can be viewed as a unified framework formulti-view learning since it can handle both incomplete and complete multi-viewclustering and classification tasks. Extensive experiments conducted on severalmulti-view datasets prove that the proposed method can significantly improvethe clustering performance.

Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization