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Fast Multi-View Outlier Detection via Deep Encoder
Hou DD(侯冬冬)1,2,3; Cong Y(丛杨)1,2; Sun G(孙干)1,2; Dong JH(董家华)1,2,3; Li, Jun4; Li, Kai5
Source PublicationIEEE Transactions on Big Data
Indexed ByEI
EI Accession number20202808916701
Contribution Rank1
Funding OrganizationMinistry of Science and Technology of the People´s Republic of China (2019YFB1310300) ; National Nature Science Foundation of China under Grant (61722311, U1613214, 61821005, 61533015)
KeywordOutlier detection Multiple views Large-scale dataset Deep encoder

Multi-view outlier detection has been well investigated in recent years. However, 1) most existing methods cannot efficiently handle outlier detection problem for large-scale multi-view data, since exploring pairwise constraints among different views causes highly-computational cost; 2) the data collected from heterogeneous feature spaces further increases the difficulty of multi-view outlier detection. To address these issues, we present a fast multi-view outlier detection model via learning a low-rank latent subspace representation with deep encoder architecture, which can not only identify the outliers for large-scale data even with numerous data views, but also exploit a common latent subspace shared by all views. First, we learn a view-specific dictionaries from a small dataset sampled from original dataset. Benefiting from view-specific dictionaries, the sampled data is projected as a shared and discriminative latent representations, which correspond to view-consistent and view-specific components across multiple views, respectively. Then, the obtained discriminative representations are applied to train the view-specific deep encoders, which can efficiently compute the abnormal score for the remaining instances. Our model can cost-effectively identify outliers in large-scale datasets from numerous data views with less computational complexity. Experiments conducted on eight datasets and a synthesis dataset show that our model outperforms existing ones effectively.

Document Type期刊论文
Corresponding AuthorCong Y(丛杨)
Affiliation1.Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China
3.University of Chinese Academy of Sciences, Beijing, 100049, China
4.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
5.Department of Electrical and Computer Engineering, College of Engineering, Northeastern University, Boston, MA 02115 USA
Recommended Citation
GB/T 7714
Hou DD,Cong Y,Sun G,et al. Fast Multi-View Outlier Detection via Deep Encoder[J]. IEEE Transactions on Big Data,2020:1-11.
APA Hou DD,Cong Y,Sun G,Dong JH,Li, Jun,&Li, Kai.(2020).Fast Multi-View Outlier Detection via Deep Encoder.IEEE Transactions on Big Data,1-11.
MLA Hou DD,et al."Fast Multi-View Outlier Detection via Deep Encoder".IEEE Transactions on Big Data (2020):1-11.
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