Low-Rank Tensor Completion: A Pseudo-Bayesian Learning Approach

ICCV 2017 Wei ChenNan Song

Low rank tensor completion, which solves a linear inverse problem with the principle of parsimony, is a powerful technique used in many application domains in computer vision and pattern recognition. As a surrogate function of the matrix rank that is non-convex and discontinuous, the nuclear norm is often used instead to derive efficient algorithms for recovering missing information in matrices and higher order tensors... (read more)

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