A Fast Non-Negative Latent Factor Model Based on Generalized Momentum Method
文献类型：期刊论文
| 作者 | Luo, Xin3 ; Liu, Zhigang1; Li, Shuai4; Shang, Mingsheng1; Wang, Zidong2
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| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
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| 出版日期 | 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 |
| WOS记录号 | WOS:000607806700046 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 2.Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England 3.Dongguan Univ Technol, Sch Comp Sci & Technol, Dongguan 523808, Peoples R China 4.Hong Kong Polytech Univ, Dept Comp, Hong Kong 999077, Peoples R China |
| 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. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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