ShanghaiTech University Knowledge Management System
An Outlier Cleaning Algorithm Based on Deep Learning | |
2022-02 | |
发表期刊 | 电子与信息学报 (IF:0.5[JCR-2023],0.4[5-Year]) |
ISSN | 1009-5896 |
卷号 | 44期号:2页码:507-513 |
发表状态 | 已发表 |
DOI | 10.11999/JEIT201097 |
摘要 | The use of appropriate abnormal data cleaning algorithms in the Internet of Things (IoT) can greatly improve data quality. Statistical methods or clustering methods are utilized to clean anomalies in Spatio-temporal data. However, these methods require additional prior knowledge, which will incur additional computational overhead for the sink node. In this paper, in line with the low-rank sparse matrix decomposition model, a fast anomaly cleaning algorithm based on a deep neural network is proposed to solve the Spatio-temporal data cleaning problem in IoT. Both the Spatio-temporal correlation of sensing data and the abnormal values' sparsity are considered in an optimization problem. The Iterative Shrinkage-Thresholding Algorithm (ISTA) is used to solve it. Then the ISTA is unfolded into a fixed-length deep neural network. The real-world dataset's experimental results show that the proposed method can automatically update the thresholds faster and more accurately than the traditional ISTA. © 2022, Science Press. All right reserved. |
关键词 | Big data Cleaning Internet of things Iterative methods Shrinkage Abnormal data Clustering methods Data cleaning Data quality Internet of thing Iterative shrinkage-thresholding algorithm Iterative shrinkagethresholding algorithms Outlier cleaning Spatio-temporal data Unfoldings |
收录类别 | EI ; ESCI ; 北大核心 |
语种 | 中文 |
出版者 | Science Press |
EI入藏号 | 20220911731990 |
EI主题词 | Deep neural networks |
EI分类号 | 461.4 Ergonomics and Human Factors Engineering ; 722.3 Data Communication, Equipment and Techniques ; 723 Computer Software, Data Handling and Applications ; 723.2 Data Processing and Image Processing ; 802.3 Chemical Operations ; 921.6 Numerical Methods ; 951 Materials Science |
原始文献类型 | Journal article (JA) |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/159567 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_特聘教授组_钱骅组 |
通讯作者 | Qian, Hua |
作者单位 | 1.Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai; 201210, China; 2.School of Information Science and Technology, ShanghaiTech University, Shanghai; 201210, China; 3.University of Chinese Academy of Sciences, Beijing; 100049, China; 4.School of Microelectronics, University of Chinese Academy of Sciences, Beijing; 100049, China; 5.Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai; 200050, China |
第一作者单位 | 信息科学与技术学院 |
通讯作者单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Kuang, Junqian,Zhao, Chang,Yang, Liu,et al. An Outlier Cleaning Algorithm Based on Deep Learning[J]. 电子与信息学报,2022,44(2):507-513. |
APA | Kuang, Junqian,Zhao, Chang,Yang, Liu,Wang, Haifeng,&Qian, Hua.(2022).An Outlier Cleaning Algorithm Based on Deep Learning.电子与信息学报,44(2),507-513. |
MLA | Kuang, Junqian,et al."An Outlier Cleaning Algorithm Based on Deep Learning".电子与信息学报 44.2(2022):507-513. |
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