An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications

doi:10.1109/TII.2017.2766528

	An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications
	Luo, Xin1 ; Zhou, Mengchu 2; Li, Shuai 3,4; Shang, Mingsheng1
刊名	IEEE Transactions on Industrial Informatics
	2018
卷号	14 期号:5 页码:2011-2022
ISSN号	15513203
DOI	10.1109/TII.2017.2766528
英文摘要	High-dimensional and sparse (HiDS) matrices are commonly encountered in many big-data-related and industrial applications like recommender systems. When acquiring useful patterns from them, nonnegative matrix factorization (NMF) models have proven to be highly effective owing to their fine representativeness of the nonnegative data. However, current NMF techniques suffer from: 1) inefficiency in addressing HiDS matrices; and 2) constraints in their training schemes. To address these issues, this paper proposes to extract nonnegative latent factors (NLFs) from HiDS matrices via a novel inherently NLF (INLF) model. It bridges the output factors and decision variables via a single-element-dependent mapping function, thereby making the parameter training unconstrained and compatible with general training schemes on the premise of maintaining the nonnegativity constraints. Experimental results on six HiDS matrices arising from industrial applications indicate that INLF is able to acquire NLFs from them more efficiently than any existing method does. © 2005-2012 IEEE.
语种	英语
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/8021]
专题	中国科学院重庆绿色智能技术研究院
作者单位	1.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China; 2.Institute of Systems Engineering, Macau University of Science and Technology, 999078, China; 3.Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark; NJ; 07102, United States; 4.Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong
推荐引用方式 GB/T 7714	Luo, Xin,Zhou, Mengchu,Li, Shuai,et al. An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications[J]. IEEE Transactions on Industrial Informatics,2018,14(5):2011-2022.
APA	Luo, Xin,Zhou, Mengchu,Li, Shuai,&Shang, Mingsheng.(2018).An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications.IEEE Transactions on Industrial Informatics,14(5),2011-2022.
MLA	Luo, Xin,et al."An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications".IEEE Transactions on Industrial Informatics 14.5(2018):2011-2022.