Multi-view laplacian least squares for human emotion recognition
文献类型：期刊论文
| 作者 | Guo, Shuai1; Feng, Lin1; Feng, Zhan-Bo2; Li, Yi-Hao2; Wang, Yang1 ; Liu, Sheng-Lan1; Qiao, Hong3
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2019-12-22 |
| 卷号 | 370页码:78-87 |
| 关键词 | Multi-view learning Laplacian least squares Subspace learning Human emotion recognition |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.07.049 |
| 通讯作者 | Feng, Lin(fenglin@dlut.edu.cn) |
| 英文摘要 | Human 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. |
| WOS关键词 | CANONICAL CORRELATION-ANALYSIS |
| 资助项目 | 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] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000493285800007 |
| 出版者 | ELSEVIER |
| 资助机构 | National 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28836]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 通讯作者 | Feng, Lin |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。