Tensor Multi-Elastic Kernel Self-Paced Learning for Time Series Clustering
文献类型：期刊论文
| 作者 | Tang, Yongqiang2,3 ; Xie, Yuan1; Yang, Xuebing2; Niu, Jinghao2,3; Zhang, Wensheng2,3
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| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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| 出版日期 | 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 |
| WOS记录号 | WOS:000615042700030 |
| 出版者 | IEEE COMPUTER SOC |
| 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 |
| 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 自动化研究所_精密感知与控制研究中心 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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