Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning
文献类型：期刊论文
| 作者 | Tian, Lei1,2 ; Tang, Yongqiang2; Hu, Liangchen3; Ren, Zhida1,2; Zhang, Wensheng1,2
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2020 |
| 卷号 | 29页码:9703-9718 |
| 关键词 | Domain adaptation class centroid matching local manifold self-learning |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2020.3031220 |
| 通讯作者 | Zhang, Wensheng(zhangwenshengia@hotmail.com) |
| 英文摘要 | Domain adaptation has been a fundamental technology for transferring knowledge from a source domain to a target domain. The key issue of domain adaptation is how to reduce the distribution discrepancy between two domains in a proper way such that they can be treated indifferently for learning. In this paper, we propose a novel domain adaptation approach, which can thoroughly explore the data distribution structure of target domain. Specifically, we regard the samples within the same cluster in target domain as a whole rather than individuals and assigns pseudo-labels to the target cluster by class centroid matching. Besides, to exploit the manifold structure information of target data more thoroughly, we further introduce a local manifold self-learning strategy into our proposal to adaptively capture the inherent local connectivity of target samples. An efficient iterative optimization algorithm is designed to solve the objective function of our proposal with theoretical convergence guarantee. In addition to unsupervised domain adaptation, we further extend our method to the semi-supervised scenario including both homogeneous and heterogeneous settings in a direct but elegant way. Extensive experiments on seven benchmark datasets validate the significant superiority of our proposal in both unsupervised and semi-supervised manners. |
| WOS关键词 | KERNEL |
| 资助项目 | Key-Area Research and Development Program of Guangdong Province[2019B010153002] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61772525] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000583696200003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/41752]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Zhang, Wensheng |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China 3.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tian, Lei,Tang, Yongqiang,Hu, Liangchen,et al. Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:9703-9718. |
| APA | Tian, Lei,Tang, Yongqiang,Hu, Liangchen,Ren, Zhida,&Zhang, Wensheng.(2020).Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,9703-9718. |
| MLA | Tian, Lei,et al."Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):9703-9718. |
入库方式： OAI收割
来源：自动化研究所
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