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• W2149403928 abstract "Low-rank tensor estimation has been frequently applied in many real-world problems. Despite successful applications, existing Schatten 1-norm minimization (SNM) methods may become very slow or even not applicable for large-scale problems. To address this difficulty, we therefore propose an efficient and scalable core tensor Schatten 1-norm minimization method for simultaneous tensor decomposition and completion, with a much lower computational complexity. We first induce the equivalence relation of Schatten 1-norm of a low-rank tensor and its core tensor. Then the Schatten 1-norm of the core tensor is used to replace that of the whole tensor, which leads to a much smaller-scale matrix SNM problem. Finally, an efficient algorithm with a rank-increasing scheme is developed to solve the proposed problem with a convergence guarantee. Extensive experimental results show that our method is usually more accurate than the state-of-the-art methods, and is orders of magnitude faster." @default.
• W2149403928 created "2016-06-24" @default.
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• W2149403928 date "2014-12-08" @default.
• W2149403928 modified "2023-10-11" @default.
• W2149403928 title "Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion" @default.
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• W2149403928 hasPublicationYear "2014" @default.
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