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Multi-Aspect Embedding for Attribute-Aware Trajectories

文献类型：期刊论文

作者Boonchoo, Thapana1,2; Ao, Xiang1,2; He, Qing1,2
刊名SYMMETRY-BASEL
出版日期2019-09-01
卷号11期号:9页码:17
关键词trajectory similarity computation multi-aspect embedding representation learning
DOI10.3390/sym11091149
英文摘要Motivated by the proliferation of trajectory data produced by advanced GPS-enabled devices, trajectory is gaining in complexity and beginning to embroil additional attributes beyond simply the coordinates. As a consequence, this creates the potential to define the similarity between two attribute-aware trajectories. However, most existing trajectory similarity approaches focus only on location based proximities and fail to capture the semantic similarities encompassed by these additional asymmetric attributes (aspects) of trajectories. In this paper, we propose multi-aspect embedding for attribute-aware trajectories (MAEAT), a representation learning approach for trajectories that simultaneously models the similarities according to their multiple aspects. MAEAT is built upon a sentence embedding algorithm and directly learns whole trajectory embedding via predicting the context aspect tokens when given a trajectory. Two kinds of token generation methods are proposed to extract multiple aspects from the raw trajectories, and a regularization is devised to control the importance among aspects. Extensive experiments on the benchmark and real-world datasets show the effectiveness and efficiency of the proposed MAEAT compared to the state-of-the-art and baseline methods. The results of MAEAT can well support representative downstream trajectory mining and management tasks, and the algorithm outperforms other compared methods in execution time by at least two orders of magnitude.
资助项目National Key Research and Development Program of China[2017YFB1002104] ; National Natural Science Foundation of China[U1811461] ; National Natural Science Foundation of China[61602438] ; National Natural Science Foundation of China[91846113] ; National Natural Science Foundation of China[61573335] ; Project of Youth Innovation Promotion Association CAS
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000489177900085
出版者MDPI
源URL[http://119.78.100.204/handle/2XEOYT63/4628]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Boonchoo, Thapana; He, Qing
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Boonchoo, Thapana,Ao, Xiang,He, Qing. Multi-Aspect Embedding for Attribute-Aware Trajectories[J]. SYMMETRY-BASEL,2019,11(9):17.
APA Boonchoo, Thapana,Ao, Xiang,&He, Qing.(2019).Multi-Aspect Embedding for Attribute-Aware Trajectories.SYMMETRY-BASEL,11(9),17.
MLA Boonchoo, Thapana,et al."Multi-Aspect Embedding for Attribute-Aware Trajectories".SYMMETRY-BASEL 11.9(2019):17.

入库方式： OAI收割

来源：计算技术研究所

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