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Low-Rank Tensor Thresholding Ridge Regression
2019
IEEE Access
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
doi:10.1109/access.2019.2944426
fatcat:56fatkxvabbzrchvsdotsjjzp4