n-Mode Singular Vector Selection in Higher-Order Singular Value Decomposition

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In this paper, we propose a method for selecting n-mode singular vectors in higher-order singular value decomposition. We select the minimum number of n-mode singular vectors, when the upper bound of a least-squares cost function is thresholded. The reduced n-ranks of all modes of a given tensor are determined automatically and the tensor is represented with the minimum number of dimensions. We apply the selection method to simultaneous low rank approximation of matrices. Experimental results show the effectiveness of the n-mode singular vector selection method.

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