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

doi:10.1109/TKDE.2019.2937027

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering
	Tang, Yongqiang1,2 ; Xie, Yuan3 ; Yang, Xuebing1 ; Niu, Jinghao1,2 ; Zhang, Wensheng1,2
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
	2021-03-01
卷号	33 期号:3 页码:1223-1237
通讯作者	Tang, Yongqiang(tangyongqiang2014@ia.ac.cn)
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.
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
DOI	10.1109/TKDE.2019.2937027
收录类别	SCI
语种	英语
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
项目资助者	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Fundamental Research Funds for the Central Universities ; Shanghai Key Laboratory of Trustworthy Computing
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000615042700030
出版者	IEEE COMPUTER SOC
七大方向——子方向分类	机器学习
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/43272
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队精密感知与控制研究中心
通讯作者	Tang, Yongqiang
作者单位	1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.East China Normal Univ, Sch Comp Sci & Technol, Shanghai 200241, Peoples R China
第一作者单位	精密感知与控制研究中心
通讯作者单位	精密感知与控制研究中心
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.