Multi-view subspace clustering aims to divide a set of multi-source data into several groups according to their
underlying subspace structure. However, the similarity matrix which is learned by most existing methods, can not well
characterize data themselves more comprehensively in original data space and the global similarity the multi-view data.
In this paper, we propose to jointly learn the shared latent representation by different views and the similarity matrix in a
unified model. Our model can learn the shared latent representation of multi-view data from the latent space and the
similarity matrix from the shared latent representation simultaneously. Experimental results on four benchmark datasets
demonstrate that our method outperforms other existing competitive multi-view clustering methods.
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