Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering

doi:10.1109/TKDE.2019.2937027

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	Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering
	Tang, Yongqiang 2,3; Xie, Yuan 1; Yang, Xuebing 2; Niu, Jinghao 2,3; Zhang, Wensheng 2,3
刊名	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
	2021-03-01
卷号	33 期号:3 页码:1223-1237
关键词	Kernel Time series analysis Time measurement Clustering algorithms Optimization Task analysis Time series clustering multiple kernels clustering self-paced learning tensor optimization
ISSN号	1041-4347
DOI	10.1109/TKDE.2019.2937027
通讯作者	Tang, Yongqiang(tangyongqiang2014@ia.ac.cn)
英文摘要	Time series clustering has attracted growing attention due to the abundant data accessible and extensive value in various applications. The unique characteristics of time series, including high-dimension, warping, and the integration of multiple elastic measures, pose challenges for the present clustering algorithms, most of which take into account only part of these difficulties. In this paper, we make an effort to simultaneously address all aforementioned issues in time series clustering under a unified multiple kernels clustering (MKC) framework. Specifically, we first implicitly map the raw time series space into multiple kernel spaces via elastic distance measure functions. In such high-dimensional spaces, we resort to the tensor constraint based self-representation subspace clustering approach, which involves the self-paced learning paradigm, to explore the essential low-dimensional structure of the data, as well as the high-order complementary information from different elastic kernels. The proposed approach can be extended to more challenging multivariate time series clustering scenario in a direct but elegant way. Extensive experiments on 85 univariate and 10 multivariate time series datasets demonstrate the significant superiority of the proposed approach beyond the baseline and several state-of-the-art MKC methods.
资助项目	National Natural Science Foundation of China[61432008] ; National Natural Science Foundation of China[61472423] ; National Natural Science Foundation of China[61602484] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61772524] ; Beijing Municipal Natural Science Foundation[4182067] ; Fundamental Research Funds for the Central Universities ; Shanghai Key Laboratory of Trustworthy Computing
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE COMPUTER SOC
WOS记录号	WOS:000615042700030
资助机构	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Fundamental Research Funds for the Central Universities ; Shanghai Key Laboratory of Trustworthy Computing
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/43271]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Tang, Yongqiang
作者单位	1.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200241, Peoples R China 2.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Tang, Yongqiang,Xie, Yuan,Yang, Xuebing,et al. Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2021,33(3):1223-1237.
APA	Tang, Yongqiang,Xie, Yuan,Yang, Xuebing,Niu, Jinghao,&Zhang, Wensheng.(2021).Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,33(3),1223-1237.
MLA	Tang, Yongqiang,et al."Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 33.3(2021):1223-1237.