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Learning deep representation for trajectory clustering

文献类型：期刊论文

作者Yao, Di2,5; Zhang, Chao3; Zhu, Zhihua2,5; Hu, Qin1; Wang, Zheng4; Huang, Jianhui2,5; Bi, Jingping2,5
刊名EXPERT SYSTEMS
出版日期2018-04-01
卷号35期号:2页码:16
关键词recurrent neural network representation learning sequence-to-sequence learning trajectory clustering
ISSN号0266-4720
DOI10.1111/exsy.12252
英文摘要Trajectory clustering, which aims at discovering groups of similar trajectories, has long been considered as a corner stone task for revealing movement patterns as well as facilitating higher level applications such as location prediction and activity recognition. Although a plethora of trajectory clustering techniques have been proposed, they often rely on spatio-temporal similarity measures that are not space and time invariant. As a result, they cannot detect trajectory clusters where the within-cluster similarity occurs in different regions and time periods. In this paper, we revisit the trajectory clustering problem by learning quality low-dimensional representations of the trajectories. We first use a sliding window to extract a set of moving behaviour features that capture space- and time-invariant characteristics of the trajectories. With the feature extraction module, we transform each trajectory into a feature sequence to describe object movements and further employ a sequence-to-sequence auto-encoder to learn fixed-length deep representations. The learnt representations robustly encode the movement characteristics of the objects and thus lead to space- and time-invariant clusters. We evaluate the proposed method on both synthetic and real data and observe significant performance improvements over existing methods.
资助项目National Natural Science Foundation of China (NSFC)[61303243] ; National Natural Science Foundation of China (NSFC)[61472403]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000430915400008
出版者WILEY
源URL[http://119.78.100.204/handle/2XEOYT63/5370]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Bi, Jingping
作者单位1.Beijing Normal Univ, Coll Informat Sci & Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
3.Univ Illinois, Dept Comp Sci, Urbana, IL USA
4.Univ Sydney, Sch Informat Technol, Sydney, NSW, Australia
5.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Yao, Di,Zhang, Chao,Zhu, Zhihua,et al. Learning deep representation for trajectory clustering[J]. EXPERT SYSTEMS,2018,35(2):16.
APA Yao, Di.,Zhang, Chao.,Zhu, Zhihua.,Hu, Qin.,Wang, Zheng.,...&Bi, Jingping.(2018).Learning deep representation for trajectory clustering.EXPERT SYSTEMS,35(2),16.
MLA Yao, Di,et al."Learning deep representation for trajectory clustering".EXPERT SYSTEMS 35.2(2018):16.

入库方式： OAI收割

来源：计算技术研究所

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