On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization

23 Oct 2016 Yuan Xie DaCheng Tao Wensheng Zhang Lei Zhang Yan Liu Yanyun Qu

In this paper, we address the multi-view subspace clustering problem. Our method utilizes the circulant algebra for tensor, which is constructed by stacking the subspace representation matrices of different views and then rotating, to capture the low rank tensor subspace so that the refinement of the view-specific subspaces can be achieved, as well as the high order correlations underlying multi-view data can be explored.}.. (read more)

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