| Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition | |
Tao, Dapeng1; Jin, Lianwen1; Yuan, Yuan2 ; Xue, Yang1
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| 作者部门 | 光学影像学习与分析中心 |
| 2016-06 | |
| 发表期刊 | IEEE Transactions on Neural Networks and Learning Systems
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| ISSN | 2162237X |
| 卷号 | 27期号:6页码:1392-1404 |
| 产权排序 | 2 |
| 摘要 | With the rapid development of mobile devices and pervasive computing technologies, acceleration;based human activity recognition, a difficult yet essential problem in mobile apps, has received intensive attention recently. Different acceleration signals for representing different activities or even a same activity have different attributes, which causes troubles in normalizing the signals. We thus cannot directly compare these signals with each other, because they are embedded in a nonmetric space. Therefore, we present a nonmetric scheme that retains discriminative and robust frequency domain information by developing a novel ensemble manifold rank preserving (EMRP) algorithm. EMRP simultaneously considers three aspects: 1) it encodes the local geometry using the ranking order information of intraclass samples distributed on local patches; 2) it keeps the discriminative information by maximizing the margin between samples of different classes; and 3) it finds the optimal linear combination of the alignment matrices to approximate the intrinsic manifold lied in the data. Experiments are conducted on the South China University of Technology naturalistic 3;D acceleration;based activity dataset and the naturalistic mobile;devices based human activity dataset to demonstrate the robustness and effectiveness of the new nonmetric scheme for acceleration;based human activity recognition. © 2012 IEEE. |
| 关键词 | Frequency Domain Analysis Mobile Devices Pattern Recognition Ubiquitous Computing |
| DOI | 10.1109/TNNLS.2014.2357794 |
| 收录类别 | EI |
| 语种 | 英语 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/28253 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.School of Electronic and Information Engineering, South China University of Technology, Guangzhou; 510641, China 2.Center for OPTical IMagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Xi'an; 710119, China |
| 推荐引用方式 GB/T 7714 | Tao, Dapeng,Jin, Lianwen,Yuan, Yuan,et al. Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition[J]. IEEE Transactions on Neural Networks and Learning Systems,2016,27(6):1392-1404. |
| APA | Tao, Dapeng,Jin, Lianwen,Yuan, Yuan,&Xue, Yang.(2016).Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition.IEEE Transactions on Neural Networks and Learning Systems,27(6),1392-1404. |
| MLA | Tao, Dapeng,et al."Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition".IEEE Transactions on Neural Networks and Learning Systems 27.6(2016):1392-1404. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Ensemble Manifold Ra（3478KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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