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Multiview Hybrid Embedding: A Divide-and-Conquer Approach
Xu, Jiamiao1; Yu, Shujian2; You, Xinge1; Leng, Mengjun3; Jing, Xiao Yuan4; Chen, C. L.Philip5
Source PublicationIEEE Transactions on Cybernetics
ABS Journal Level3

We present a novel cross-view classification algorithm where the gallery and probe data come from different views. A popular approach to tackle this problem is the multiview subspace learning (MvSL) that aims to learn a latent subspace shared by multiview data. Despite promising results obtained on some applications, the performance of existing methods deteriorates dramatically when the multiview data is sampled from nonlinear manifolds or suffers from heavy outliers. To circumvent this drawback, motivated by the Divide-and-Conquer strategy, we propose multiview hybrid embedding (MvHE), a unique method of dividing the problem of cross-view classification into three subproblems and building one model for each subproblem. Specifically, the first model is designed to remove view discrepancy, whereas the second and third models attempt to discover the intrinsic nonlinear structure and to increase the discriminability in intraview and interview samples, respectively. The kernel extension is conducted to further boost the representation power of MvHE. Extensive experiments are conducted on four benchmark datasets. Our methods demonstrate the overwhelming advantages against the state-of-the-art MvSL-based cross-view classification approaches in terms of classification accuracy and robustness.

KeywordCross-view Classification Divide-and-conquer Multiview Hybrid Embedding (Mvhe) Multiview Learning
URLView the original
Indexed BySCIE
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000548811800019
Scopus ID2-s2.0-85088205763
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Cited Times [WOS]:27   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorYou, Xinge
Affiliation1.School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
2.Department of Electrical and Computer Engineering, University of Florida, Gainesville, United States
3.Department of Computer Science, University of Houston, Houston, United States
4.State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, China
5.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macao
Recommended Citation
GB/T 7714
Xu, Jiamiao,Yu, Shujian,You, Xinge,et al. Multiview Hybrid Embedding: A Divide-and-Conquer Approach[J]. IEEE Transactions on Cybernetics,2020,50(8):3640-3653.
APA Xu, Jiamiao,Yu, Shujian,You, Xinge,Leng, Mengjun,Jing, Xiao Yuan,&Chen, C. L.Philip.(2020).Multiview Hybrid Embedding: A Divide-and-Conquer Approach.IEEE Transactions on Cybernetics,50(8),3640-3653.
MLA Xu, Jiamiao,et al."Multiview Hybrid Embedding: A Divide-and-Conquer Approach".IEEE Transactions on Cybernetics 50.8(2020):3640-3653.
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