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Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data

文献类型：期刊论文

作者Luo, Xin3,4; Chen, Minzhi2,3; Wu, Hao1,7,9; Liu, Zhigang1,7,9; Yuan, Huaqiang4; Zhou, Mengchu5,6,8
刊名IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
出版日期2021-10-01
卷号18期号:4页码:2142-2155
关键词Tensors Data models Quality of service Computational modeling Analytical models Training Web services Algorithm big data dynamics high-dimensional and incomplete (HDI) data machine learning missing data estimation multichannel data nonnegative latent factorization of tensors (NLFT) temporal pattern quality of service (QoS) web service
ISSN号1545-5955
DOI10.1109/TASE.2020.3040400
通讯作者Yuan, Huaqiang(yuanhq@dgut.edu.cn) ; Zhou, Mengchu(zhou@njit.edu)
英文摘要A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal patterns hidden in multichannel data emerging from various applications. It often adopts a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. However, learning depth in this algorithm is not adjustable, resulting in frequent training fluctuation or poor model convergence caused by overshooting. To address this issue, this study carefully investigates the connections between the performance of an NLFT model and its learning depth via SLF-NMUT to present a joint learning-depth-adjusting scheme for it. Based on this scheme, a Depth-adjusted Multiplicative Update on tensor algorithm is innovatively proposed, thereby achieving a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Empirical studies on two industrial data sets demonstrate that compared with the state-of-the-art NLFT models, a DNL model achieves significant accuracy gain when performing missing data estimation on a high-dimensional and incomplete tensor with high efficiency. Note to Practitioners-Multichannel data are often encountered in various big-data-related applications. It is vital for a data analyzer to correctly capture the temporal patterns hidden in them for efficient knowledge acquisition and representation. This article focuses on analyzing temporal QoS data, which is a representative kind of multichannel data. To correctly extract their temporal patterns, an analyzer should correctly describe their nonnegativity. Such a purpose can be achieved by building a nonnegative latent factorization of tensors (NLFT) model relying on a single latent factor-dependent, nonnegative and multiplicative update on tensor (SLF-NMUT) algorithm. But its learning depth is not adjustable, making an NLFT model frequently suffer from severe fluctuations in its training error or even fail to converge. To address this issue, this study carefully investigates the learning rules for an NLFT model's decision parameters using an SLF-NMUT and proposes a joint learning-depth-adjusting scheme. This scheme manipulates the multiplicative terms in SLF-NMUT-based learning rules linearly and exponentially, thereby making the learning depth adjustable. Based on it, this study builds a novel depth-adjusted nonnegative latent-factorization-of-tensors (DNL) model. Compared with the existing NLFT models, a DNL model better represents multichannel data. It meets industrial needs well and can be used to achieve high performance in data analysis tasks like temporal-aware missing data estimation
资助项目National Natural Science Foundation of China[61772493] ; Guangdong Province Universities and College Pearl River Scholar Funded Scheme (2019) ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013]
WOS研究方向Automation & Control Systems
语种英语
WOS记录号WOS:000704116700053
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/14275]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Yuan, Huaqiang; Zhou, Mengchu
作者单位1.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
2.Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China
3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
4.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Peoples R China
5.Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau 999078, Peoples R China
6.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
7.Univ Chinese Acad Sci, Chongqing Sch, Chongqing 400714, Peoples R China
8.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
9.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Luo, Xin,Chen, Minzhi,Wu, Hao,et al. Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,2021,18(4):2142-2155.
APA Luo, Xin,Chen, Minzhi,Wu, Hao,Liu, Zhigang,Yuan, Huaqiang,&Zhou, Mengchu.(2021).Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data.IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING,18(4),2142-2155.
MLA Luo, Xin,et al."Adjusting Learning Depth in Nonnegative Latent Factorization of Tensors for Accurately Modeling Temporal Patterns in Dynamic QoS Data".IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 18.4(2021):2142-2155.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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