Fast low-rank matrix approximation with locality sensitive hashing for quick anomaly detection-Reference-Cited by-同舟云学术

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Fast low-rank matrix approximation with locality sensitive hashing for quick anomaly detection

Published:2017-05 Issue: Volume: Page:
ISSN:
Container-title:IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
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Author:

Xie Gaogang,Xie Kun,Huang Jun,Wang Xin,Chen Yuxiang,Wen Jigang

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8049192/8056940/08057217.pdf?arnumber=8057217

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. Neighbor Graph Based Tensor Recovery For Accurate Internet Anomaly Detection;IEEE Transactions on Parallel and Distributed Systems;2023-02-01

3. A Novel Sequence Tensor Recovery Algorithm for Quick and Accurate Anomaly Detection;IEEE Transactions on Network Science and Engineering;2022-09-01

4. MT-FlowFormer: A Semi-Supervised Flow Transformer for Encrypted Traffic Classification;Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2022-08-14

5. SNIFF: A Scalable Network Inference Framework for Measuring End-to-End Performance;IEEE Transactions on Network Science and Engineering;2022-05-01

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