CASIA OpenIR
Multi-view laplacian least squares for human emotion recognition
Guo, Shuai1; Feng, Lin1; Feng, Zhan-Bo2; Li, Yi-Hao2; Wang, Yang1; Liu, Sheng-Lan1; Qiao, Hong3
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-12-22
Volume370Pages:78-87
Corresponding AuthorFeng, Lin(fenglin@dlut.edu.cn)
AbstractHuman emotion recognition is an emerging and important area in the field of human-computer interaction and artificial intelligence, which has been more and more related with multi-view learning methods. Subspace learning is an important direction of multi-view learning. However, most existing subspace learning methods could not make full use of both category discriminant information and local neighborhood information. As a typical subspace learning method, partial least squares (PLS) performs better and more robustly than many other subspace learning methods, because PLS is optimized with iteration method. However, PLS suffers from linear relationship assumption and two-view limitation. In this paper, a new nonlinear multi-view laplacian least squares (MvLLS) is proposed. MvLLS constructs a global laplacian weighted graph (GLWP) to introduce category discriminant information as well as protects the local neighborhood information. Optimized with iteration method, MvLLS is a multi-view extension of PLS. The proposed method has great extendibility and robustness. To meet the requirements of large-scale applications, weighted local preserving embedding (WLPE) is proposed as the out-of-sample extension of MvLLS, basing on the idea of maintaining the manifold structures of original space. Finally, the proposed method is verified on three multi-view emotion recognition tasks, the experiment results validate the effectiveness and robustness of MvLLS. (C) 2019 Published by Elsevier B.V.
KeywordMulti-view learning Laplacian least squares Subspace learning Human emotion recognition
DOI10.1016/j.neucom.2019.07.049
WOS KeywordCANONICAL CORRELATION-ANALYSIS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of People's Republic of China[61672130] ; National Natural Science Foundation of People's Republic of China[61602082] ; National Natural Science Foundation of People's Republic of China[91648205] ; National Key Scientific Instrument and Equipment Development Project[61627808] ; Development of Science and Technology of Guangdong Province Special Fund Project Grants[2016B090910001] ; LiaoNing Revitalization Talents Program[XLYC180 6006] ; National Natural Science Foundation of People's Republic of China[61672130] ; National Natural Science Foundation of People's Republic of China[61602082] ; National Natural Science Foundation of People's Republic of China[91648205] ; National Key Scientific Instrument and Equipment Development Project[61627808] ; Development of Science and Technology of Guangdong Province Special Fund Project Grants[2016B090910001] ; LiaoNing Revitalization Talents Program[XLYC180 6006]
Funding OrganizationNational Natural Science Foundation of People's Republic of China ; National Key Scientific Instrument and Equipment Development Project ; Development of Science and Technology of Guangdong Province Special Fund Project Grants ; LiaoNing Revitalization Talents Program
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000493285800007
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28837
Collection中国科学院自动化研究所
Corresponding AuthorFeng, Lin
Affiliation1.Dalian Univ Technol, Sch Innovat & Entrepreneurship, Dalian 116024, Peoples R China
2.Dalian Univ Technol, Sch Elect Informat & Elect Engn, Dalian 116024, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Guo, Shuai,Feng, Lin,Feng, Zhan-Bo,et al. Multi-view laplacian least squares for human emotion recognition[J]. NEUROCOMPUTING,2019,370:78-87.
APA Guo, Shuai.,Feng, Lin.,Feng, Zhan-Bo.,Li, Yi-Hao.,Wang, Yang.,...&Qiao, Hong.(2019).Multi-view laplacian least squares for human emotion recognition.NEUROCOMPUTING,370,78-87.
MLA Guo, Shuai,et al."Multi-view laplacian least squares for human emotion recognition".NEUROCOMPUTING 370(2019):78-87.
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