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Anomaly detection via adaptive greedy model
Hou DD(侯冬冬)1,2; Cong Y(丛杨)1; Sun G(孙干)1,2; Liu J(刘霁)3; Xu XW(徐晓伟)4
Source PublicationNeurocomputing
Indexed BySCI ; EI
EI Accession number20184806141045
WOS IDWOS:000454789500034
Contribution Rank1
Funding OrganizationNatural Science Foundation of China under Grants (61722311, U1613214, 61533015) ; CAS-Youth Innovation Promotion Association Scholarship (2012163)
KeywordAnomaly detection Dictionary selection Forward–backward greedy algorithm ℓ0 norm ℓ2,0 norm
AbstractAnomaly detection is one of the fundamental problems within diverse research areas and application domains. In comparison with most sparse representation based anomaly detection methods adopting a relaxation term of sparsity via 1 norm, we propose an unsupervised anomaly detection method optimized via an adaptive greedy model based on 0 norm constraint, which is more accurate, robust and sparse in theory. Firstly for feature representation, a concise feature space is learned in an unsupervised way via stacked autoencoder network. We propose a dictionary selection model based on 2, 0 norm constraint to select an optimal small subset of the training data to construct a condense dictionary, which can improve accuracy and reduce computational burden simultaneously. Finally, each testing sample is reconstructed by 0 norm constraint based sparse representation, and anomalies are determined depending on the sparse reconstruction scores accordingly. For model optimization, an adaptive forward-backward greedy model is utilized to optimize this nonconvex problem with the theoretical guarantee. Our proposed method is evaluated with our real industrial dataset and benchmark datasets, and various experimental results demonstrate that our proposed method is comparable with conventional supervised methods and performs better than most comparative unsupervised methods.
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Document Type期刊论文
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Department of Computer Science, University of Rochester, Rochester, United States
4.Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR 72204, United States
Recommended Citation
GB/T 7714
Hou DD,Cong Y,Sun G,et al. Anomaly detection via adaptive greedy model[J]. Neurocomputing,2019,330:369-379.
APA Hou DD,Cong Y,Sun G,Liu J,&Xu XW.(2019).Anomaly detection via adaptive greedy model.Neurocomputing,330,369-379.
MLA Hou DD,et al."Anomaly detection via adaptive greedy model".Neurocomputing 330(2019):369-379.
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