Cross-domain activity recognition via substructural optimal transport
文献类型:期刊论文
作者 | Lu, Wang2,3; Chen, Yiqiang2,3; Wang, Jindong1; Qin, Xin2,3 |
刊名 | NEUROCOMPUTING |
出版日期 | 2021-09-24 |
卷号 | 454页码:65-75 |
ISSN号 | 0925-2312 |
关键词 | Ubiquitous computing Transfer learning Domain adaptation Optimal transport Clustering |
DOI | 10.1016/j.neucom.2021.04.124 |
英文摘要 | It is expensive and time-consuming to collect sufficient labeled data for human activity recognition (HAR). Domain adaptation is a promising approach for cross-domain activity recognition. Existing methods mainly focus on adapting cross-domain representations via domain-level, class-level, or sample-level distribution matching. However, they might fail to capture the fine-grained locality information in activity data. The domain-and class-level matching are too coarse that may result in under-adaptation, while sample-level matching may be affected by the noise seriously and eventually cause over-adaptation. In this paper, we propose substructure-level matching for domain adaptation (SSDA) to better utilize the locality information of activity data for accurate and efficient knowledge transfer. Based on SSDA, we propose an optimal transport-based implementation, Substructural Optimal Transport (SOT), for cross domain HAR. We obtain the substructures of activities via clustering methods and seeks the coupling of the weighted substructures between different domains. We conduct comprehensive experiments on four public activity recognition datasets (i.e. UCI-DSADS, UCI-HAR, USC-HAD, PAMAP2), which demonstrates that SOT significantly outperforms other state-of-the-art methods w.r.t classification accuracy (9%+ improvement). In addition, SOT is 5x faster than traditional OT-based DA methods with the same hyper-parameters. (c) 2021 Elsevier B.V. All rights reserved. |
资助项目 | KeyArea Research and Development Program of Guangdong Province[2019B010109001] ; National Natural Science Foundation of China[61972383] |
WOS研究方向 | Computer Science |
语种 | 英语 |
出版者 | ELSEVIER |
WOS记录号 | WOS:000672469900007 |
源URL | [http://119.78.100.204/handle/2XEOYT63/17526] |
专题 | 中国科学院计算技术研究所期刊论文_英文 |
通讯作者 | Chen, Yiqiang |
作者单位 | 1.Microsoft Res Asia, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Lu, Wang,Chen, Yiqiang,Wang, Jindong,et al. Cross-domain activity recognition via substructural optimal transport[J]. NEUROCOMPUTING,2021,454:65-75. |
APA | Lu, Wang,Chen, Yiqiang,Wang, Jindong,&Qin, Xin.(2021).Cross-domain activity recognition via substructural optimal transport.NEUROCOMPUTING,454,65-75. |
MLA | Lu, Wang,et al."Cross-domain activity recognition via substructural optimal transport".NEUROCOMPUTING 454(2021):65-75. |
入库方式: OAI收割
来源:计算技术研究所
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。