| Summary: | In the area of subspace clustering, methods combining self-representation and spectral clustering are predominant in recent years. For dealing with tensor data, most existing methods vectorize them into vectors and lose most of the spatial information. For removing noise of the data, most existing methods focus on the input space and lack consideration of the projection space. Aiming at preserving the spatial information of tensor data, we incorporate tensor mode-d product with low-rank matrices for self-representation. At the same time, we remove noise of the data in both the input space and the projection space, and obtain a robust affinity matrix for spectral clustering. Extensive experiments on several popular subspace clustering datasets show that the proposed method outperforms both traditional subspace clustering methods and recent state-of-the-art deep learning methods.
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