Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications
文献类型：期刊论文
| 作者 | Luo, Xin1,2 ; Sun, Jianpei1; Wang, Zidong3; Li, Shuai4; Shang, Mingsheng1
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
 |
| 出版日期 | 2017-12-01 |
| 卷号 | 13期号:6页码:3098-3107 |
| 关键词 | Big data application high-dimensional, and sparse (SHiDS) matrix nonnegative latent factor (NLF) model symmetry undirected HiDS network |
| ISSN号 | 1551-3203 |
| DOI | 10.1109/TII.2017.2724769 |
| 英文摘要 | Undirected, high-dimensional, and sparse (HiDS) networks are frequently encountered in industrial applications. They contain rich knowledge regarding various useful patterns. Nonnegative latent factor (NLF) models are effective and efficient in extracting useful knowledge from directed networks. However, they cannot describe the symmetry of an undirected network. For addressing this issue, this paper analyzes the extraction process of NLFs on asymmetric and symmetric matrices, respectively, thereby innovatively achieving the symmetric and nonnegative latent factor (SNLF) models for undirected, HiDS networks. The proposed SNLF models are equipped with: 1) high efficiency; 2) nonnegativity; and 3) symmetry. Experimental results on real networks show that the SNLF models are able to: 1) describe the symmetry of the target network rigorously; 2) ensure the nonnegativity of resultant latent factors; and 3) achieve high computational efficiency when addressing data analysis tasks like missing data estimation. |
| 资助项目 | Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Royal Society of the U.K.[61611130209] ; National Key Research and Development Program of China[2017YFC0804002] ; Young Scientist Foundation of Chongqing[cstc2014kjrc-qnrc40005] ; National Natural Science Foundation of China[61611130209] ; National Natural Science Foundation of China[61370150] ; National Natural Science Foundation of China[61433014] ; National Natural Science Foundation of China[61402198] |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000418128400032 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://172.16.51.4:88/handle/2HOD01W0/108]  |
| 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.Shenzhen Univ, Coll Comp Sci & Engn, Shenzhen 518060, Peoples R China 3.Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England 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,Sun, Jianpei,Wang, Zidong,et al. Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2017,13(6):3098-3107. |
| APA | Luo, Xin,Sun, Jianpei,Wang, Zidong,Li, Shuai,&Shang, Mingsheng.(2017).Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,13(6),3098-3107. |
| MLA | Luo, Xin,et al."Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 13.6(2017):3098-3107. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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