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• W4312794626 abstract "The Tucker Decomposition (TKD) is able to provide the low-dimensional and informative representations of real-world large-scale tensorial data, which are necessary to extract potential features and enhance the original data. However, computing such decomposition directly for a dense tensor is usually computationally elusive, due to the repetitive operations of computing large-scale tensor-matrix product. Instead of direct decomposition, this paper proposes an efficient algorithm for seeking the Faster TKD of the large-scale tensor, which is a lightweight decomposition approach based on the technique of randomized sampling. The proposed algorithm first converts the original large-scale tensor into a small-scale subtensor via full-mode sampling operation, and then the core tensor of TKD can be computed directly based on the subtensor with low complexity. Finally, an approximate TKD of the original large-scale tensor can be obtained after sequentially computing approximate full-mode factor matrices. A theoretical error analysis is provided to show that the approximation error approximates zero with high probability, and the proposed algorithm is verified based on real tensorial data of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$text{23821.24}~GB$</tex-math></inline-formula> ." @default.
• W4312794626 created "2023-01-05" @default.
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• W4312794626 date "2022-01-01" @default.
• W4312794626 modified "2023-09-25" @default.
• W4312794626 title "Faster TKD: Towards Lightweight Decomposition for Large-Scale Tensors With Randomized Block Sampling" @default.
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• W4312794626 doi "https://doi.org/10.1109/tkde.2022.3218846" @default.
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