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Fast algorithms for Higher-order Singular Value Decomposition from incomplete data
[article]
2016
arXiv
pre-print
Higher-order singular value decomposition (HOSVD) is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of them involve incomplete data. To obtain HOSVD of the data with missing values, one can first impute the missing entries through a certain tensor completion method and then perform HOSVD to the reconstructed data. However, the two-step procedure can be inefficient and does not make
arXiv:1411.4324v2
fatcat:xipgenc5pjfj5dxyptfmsztnla