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An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications
文献类型：期刊论文
| 作者 | Luo, Xin1 ; Zhou, MengChu2,3; Li, Shuai4; Shang, MingSheng1
|
| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
 |
| 出版日期 | 2018-05-01 |
| 卷号 | 14期号:5页码:2011-2022 |
| 关键词 | Big data high-dimensional and sparse matrix learning algorithms missing-data estimation nonnegative latent factor analysis optimization methods recommender system |
| ISSN号 | 1551-3203 |
| 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. |
| 资助项目 | National Key Research and Development Program of China[2017YFC0804002] ; Royal Society of the UK[61611130209] ; National Natural Science Foundation of China[61611130209] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[51609229] ; FDCT (Fundo para o Desenvolvimento das Ciencias e da Tecnologia)[119/2014/A3] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Young Scientist Foundation of Chongqing[cstc2014kjrc-qnrc40005] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000431531400020 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/6359]  |
| 专题 | 大数据挖掘及应用中心 |
| 通讯作者 | Luo, Xin; Li, Shuai |
| 作者单位 | 1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 3.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 4.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China |
| 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. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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