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• W2894882275 abstract "Recently a deterministic method, frequent directions (FD) is proposed to solve the high dimensional low rank approximation problem. It works well in practice, but experiences high computational cost. In this paper, we establish a fast frequent directions algorithm for the low rank approximation problem, which implants a randomized algorithm, sparse subspace embedding (SpEmb) in FD. This new algorithm makes use of FD's natural block structure and sends more information through SpEmb to each block in FD. We prove that our new algorithm produces a good low rank approximation with a sketch of size linear on the rank approximated. Its effectiveness and efficiency are demonstrated by the experimental results on both synthetic and real world datasets, as well as applications in network analysis." @default.
• W2894882275 created "2018-10-12" @default.
• W2894882275 creator A5033910089 @default.
• W2894882275 creator A5041423958 @default.
• W2894882275 date "2019-06-01" @default.
• W2894882275 modified "2023-09-23" @default.
• W2894882275 title "A Fast Frequent Directions Algorithm for Low Rank Approximation" @default.
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• W2894882275 doi "https://doi.org/10.1109/tpami.2018.2839198" @default.
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• W2894882275 hasPublicationYear "2019" @default.
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