A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method

doi:10.1109/TSMC.2018.2875452

	A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method
	Luo, Xin 2; Liu, Zhigang 3; Li, Shuai 1; Shang, Mingsheng 3; Wang, Zidong 4
刊名	IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
	2021
卷号	51 期号:1 页码:610-620
关键词	Big data high-dimensional and sparse (HiDS) matrix latent factor (LF) analysis missing data estimation non-negative LF (NLF) model recommender system
ISSN号	2168-2216
DOI	10.1109/TSMC.2018.2875452
通讯作者	Luo, Xin(luoxin21@dgut.edu.cn)
英文摘要	Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) is an efficient algorithm for building an NLF model on an HiDS matrix, yet it suffers slow convergence. A momentum method is frequently adopted to accelerate a learning algorithm, but it is incompatible with those implicitly adopting gradients like SLF-NMU. To build a fast NLF (FNLF) model, we propose a generalized momentum method compatible with SLF-NMU. With it, we further propose a single latent factor-dependent non-negative, multiplicative and momentum-incorporated update algorithm, thereby achieving an FNLF model. Empirical studies on six HiDS matrices from industrial application indicate that an FNLF model outperforms an NLF model in terms of both convergence rate and prediction accuracy for missing data. Hence, compared with an NLF model, an FNLF model is more practical in industrial applications.
资助项目	National Key Research and Development Program of China[2017YFC0804002] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; 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:000607806700046
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/12763]
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.Hong Kong Polytech Univ, Dept Comp, Hong Kong 999077, Peoples R China 2.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, 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.Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England
推荐引用方式 GB/T 7714	Luo, Xin,Liu, Zhigang,Li, Shuai,et al. A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2021,51(1):610-620.
APA	Luo, Xin,Liu, Zhigang,Li, Shuai,Shang, Mingsheng,&Wang, Zidong.(2021).A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,51(1),610-620.
MLA	Luo, Xin,et al."A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 51.1(2021):610-620.