Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications

doi:10.1109/TCYB.2019.2894283

	Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications
	Luo, Xin 1,2,3; Zhou, MengChu 4,5; Li, Shuai 6; Hu, Lun 7; Shang, Mingsheng 2,3
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2020-05-01
卷号	50 期号:5 页码:1844-1855
关键词	Computational modeling Data models Sparse matrices Linear programming Training Convergence Analytical models Alternating-direction-method of multipliers high-dimensional and sparse matrix industrial application non-negative latent factor analysis recommender system
ISSN号	2168-2267
DOI	10.1109/TCYB.2019.2894283
通讯作者	Luo, Xin(luoxin21@dgut.edu.cn) ; Zhou, MengChu(zhou@njit.edu)
英文摘要	High-dimensional and sparse (HiDS) matrices are commonly seen in big-data-related industrial applications like recommender systems. Latent factor (LF) models have proven to be accurate and efficient in extracting hidden knowledge from them. However, they mostly fail to fulfill the non-negativity constraints that describe the non-negative nature of many industrial data. Moreover, existing models suffer from slow convergence rate. An alternating-direction-method of multipliers-based non-negative LF (AMNLF) model decomposes the task of non-negative LF analysis on an HiDS matrix into small subtasks, where each task is solved based on the latest solutions to the previously solved ones, thereby achieving fast convergence and high prediction accuracy for its missing data. This paper theoretically analyzes the characteristics of an AMNLF model, and presents detailed empirical studies regarding its performance on nine HiDS matrices from industrial applications currently in use. Therefore, its capability of addressing HiDS matrices is justified in both theory and practice.
资助项目	National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia)[119/2014/A3] ; Chongqing Research Program of Technology Innovation and Application[cstc2017rgzn-zdyfX0020] ; Chongqing Research Program of Technology Innovation and Application[cstc2017zdcy-zdyf0554] ; Chongqing Research Program of Technology Innovation and Application[cstc2017rgzn-zdyf0118] ; Chongqing Cultivation Program of Innovation and Entrepreneurship Demonstration Group[cstc2017kjrc-cxcytd0149] ; Chongqing Overseas Scholars Innovation Program[cx2017012] ; Chongqing Overseas Scholars Innovation Program[cx2018011] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000528622000006
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/10908]
专题	中国科学院重庆绿色智能技术研究院
通讯作者	Luo, Xin; Zhou, MengChu
作者单位	1.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Peoples R China 2.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 4.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 5.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 6.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China 7.Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan 430070, Peoples R China
推荐引用方式 GB/T 7714	Luo, Xin,Zhou, MengChu,Li, Shuai,et al. Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(5):1844-1855.
APA	Luo, Xin,Zhou, MengChu,Li, Shuai,Hu, Lun,&Shang, Mingsheng.(2020).Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications.IEEE TRANSACTIONS ON CYBERNETICS,50(5),1844-1855.
MLA	Luo, Xin,et al."Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications".IEEE TRANSACTIONS ON CYBERNETICS 50.5(2020):1844-1855.