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Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes

文献类型：期刊论文

作者Wu, Hao1,2,3; Luo, Xin1,2,4; Zhou, Mengchu5,6,7
刊名IEEE TRANSACTIONS ON SERVICES COMPUTING
出版日期2022-05-01
卷号15期号:3页码:1334-1344
关键词High-dimensional and sparse tensor missing data latent factor analysis temporal pattern non-negativity non-negative latent factorization of tensor regularization ensemble services computing
ISSN号1939-1374
DOI10.1109/TSC.2020.2988760
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要Dynamic relationships are frequently encountered in big data and services computing-related applications, like dynamic data of user-side QoS in Web services. They are modeled into a high-dimensional and sparse (HiDS) tensor, which contain rich knowledge regarding temporal patterns. A non-negative latent factorization of tensors (NLFT) model is very effective in extracting such patterns from an HiDS tensor. However, it commonly suffers from overfitting with improper regularization schemes. To address this issue, this article investigates NLFT models with diversified regularization schemes. Six regularized NLFT models, i.e., L-2, L-1, elastic net, log, dropout, and swish-regularized ones, are proposed and carefully investigated. Moreover, owing to their diversified regularization designs, they possess strong model diversity to achieve an effective ensemble. Empirical studies on HiDS QoS tensors from real applications demonstrate that compared with state-of-the-art models, the proposed ones better describe the temporal patterns hidden in an HiDS tensor, thereby achieving significantly higher prediction accuracy for missing data. Moreover, their ensemble further outperforms each of them in terms of prediction accuracy for missing QoS data.
资助项目National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[61602352] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000812532400013
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.138/handle/2HOD01W0/15925]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Luo, Xin
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Cloudwalk, Dept Big Data Anal Tech, Hengrui Chongqing Artificial Intelligence Res Ctr, Chongqing 401331, Peoples R China
5.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
6.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China
7.Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau 999078, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Hao,Luo, Xin,Zhou, Mengchu. Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes[J]. IEEE TRANSACTIONS ON SERVICES COMPUTING,2022,15(3):1334-1344.
APA Wu, Hao,Luo, Xin,&Zhou, Mengchu.(2022).Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes.IEEE TRANSACTIONS ON SERVICES COMPUTING,15(3),1334-1344.
MLA Wu, Hao,et al."Advancing Non-Negative Latent Factorization of Tensors With Diversified Regularization Schemes".IEEE TRANSACTIONS ON SERVICES COMPUTING 15.3(2022):1334-1344.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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