Triple Decomposition and Tensor Recovery of Third Order Tensors
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In this paper, we introduce a new tensor decomposition for third order tensors, which decomposes a third order tensor to three third order low rank tensors in a balanced way. We call such a decomposition the triple decomposition, and the corresponding rank the triple rank. We show that the triple rank of a third order tensor is not greater than the CP rank and the middle value of the Tucker rank, is strictly less than the CP rank with a substantial probability, and is strictly less than the middle value of the Tucker rank for an essential class of examples. This indicates that practical data can be approximated by low rank triple decomposition as long as it can be approximated by low rank CP or Tucker decomposition. This theoretical discovery is confirmed numerically. Numerical tests show that third order tensor data from practical applications such as internet traffic and video image are of low triple ranks. A tensor recovery method based on low rank triple decomposition is proposed. Its convergence and convergence rate are established. Numerical experiments confirm the efficiency of this method.
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