A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
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SIAM Journal on Matrix Analysis and Applications
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Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
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ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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ACM SIGGRAPH 2004 Papers
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CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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ACM Transactions on Mathematical Software (TOMS)
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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.