Learning deep representation for trajectory clustering

doi:10.1111/exsy.12252

	Learning deep representation for trajectory clustering
	Yao, Di 1,5; Zhang, Chao 2; Zhu, Zhihua 1,5; Hu, Qin 3; Wang, Zheng 4; Huang, Jianhui 1,5; Bi, Jingping 1,5
	2018-04-01
发表期刊	EXPERT SYSTEMS
ISSN	0266-4720
卷号	35 期号:2 页码:16
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.
关键词	recurrent neural network representation learning sequence-to-sequence learning trajectory clustering
DOI	10.1111/exsy.12252
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000430915400008
出版者	WILEY
引用统计	被引频次：36[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5371
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.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Illinois, Dept Comp Sci, Urbana, IL USA 3.Beijing Normal Univ, Coll Informat Sci & Technol, Beijing, Peoples R China 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.