Dimensionality reduction of data sequences for human activity recognition.
文献类型：期刊论文
| 作者 | Chen, Yen-Lun; Wu, Xinyu; Li, Teng; Cheng, Jun; Ou, Yongsheng; Xu, Mingliang |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2016 |
| 英文摘要 | Although current human activity recognition can achieve high accuracy rates, data sequences with high dimensionality are required for a reliable decision to recognize the entire activity. Traditional dimensionality reduction methods do not exploit the local geometry of classification information. In this paper, we introduce the framework of manifold elastic net that encodes the local geometry to find an aligned coordinate system for data representation. The introduced method is efficient because classification error minimization criterion is utilized to directly link the classification error with the selected subspace. In the experimental section, a dataset on human activity recognition is studied from wearable, object, and ambient sensors. (C) 2016 Elsevier B.V. All rights reserved. |
| 收录类别 | SCI |
| 原文出处 | http://www.sciencedirect.com/science/article/pii/S092523121630594X |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/9902]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | NEUROCOMPUTING |
| 推荐引用方式 GB/T 7714 | Chen, Yen-Lun,Wu, Xinyu,Li, Teng,et al. Dimensionality reduction of data sequences for human activity recognition.[J]. NEUROCOMPUTING,2016. |
| APA | Chen, Yen-Lun,Wu, Xinyu,Li, Teng,Cheng, Jun,Ou, Yongsheng,&Xu, Mingliang.(2016).Dimensionality reduction of data sequences for human activity recognition..NEUROCOMPUTING. |
| MLA | Chen, Yen-Lun,et al."Dimensionality reduction of data sequences for human activity recognition.".NEUROCOMPUTING (2016). |
入库方式： OAI收割
来源：深圳先进技术研究院
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。