Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach
文献类型：期刊论文
| 作者 | Song, Yan1; Li, Ming1; Luo, Xin2,4 ; Yang, Guisong1; Wang, Chongjing3
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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| 出版日期 | 2020-05-01 |
| 卷号 | 16期号:5页码:3006-3017 |
| 关键词 | Computational modeling Sparse matrices Biological system modeling Symmetric matrices Matrix decomposition Linear programming Convergence Big data data analysis latent factor nonnegativity sparse and large-scaled network symmetry triple-factorization undirected |
| ISSN号 | 1551-3203 |
| DOI | 10.1109/TII.2019.2908958 |
| 通讯作者 | Luo, Xin(luoxin21@cigit.ac.cn) |
| 英文摘要 | Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering are vitally important since they usually contain rich information in various patterns. Matrix factorization (MF) technique is an efficient method to extract the useful latent factors (LFs) from the LF model, which directly gives rise to the so-called MF model. However, most MF models cannot maintain some frequently encountered constraints such as nonnegativity of LFs and the symmetry of the target network. In addition, in spite of its potential capability of obtaining the effectiveness of both the computation and the storage, the currently developed double factorization (DF)-based model still suffers from the problem of the low prediction accuracy due to the limited amount of LFs. To address the above problems, a novel MF model is proposed in terms of the triple-factorization (TF) technique, thereby leading to TF-based symmetric and nonnegative latent factor (SNLF) models. Compared with the traditional DF-based SNLF model, the proposed TF-based SNLF model is equipped with: 1) constraints on symmetry and nonnegativity; 2) desirable performance with high accuracy; 3) the convergence of the algorithm; and 4) fairly low storage and computational complexity. Furthermore, in order to reduce overfitting so as to further improve the model performance, regularization is precisely considered into the proposed TF-based SNLF model. Experiments on real datasets show that the proposed TF-based SNLF model has a whelming ability of improving the estimation accuracy for the missing data as well as guaranteeing the symmetry of the target network and the nonnegativity of LFs at a little expense of the computation and storage burden. Moreover, it is easy to be implemented for the data analysis. |
| 资助项目 | Natural Science Foundation of Shanghai[18ZR1427100] ; National Natural Science Foundation of China[61772493] ; Open Project of China Academy of Aerospace Aerodynamics[20184101/20190302] ; National Basic Research Program of China[51502338] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000519588700013 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/10626]  |
| 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.Univ Shanghai Sci & Technol, Dept Control Sci & Engn, Shanghai 200093, Peoples R China 2.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 3.China Acad Informat & Commun Technol, Informatizat & Industrializat Integrat Res Inst, Beijing 100191, Peoples R China 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Yan,Li, Ming,Luo, Xin,et al. Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2020,16(5):3006-3017. |
| APA | Song, Yan,Li, Ming,Luo, Xin,Yang, Guisong,&Wang, Chongjing.(2020).Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,16(5),3006-3017. |
| MLA | Song, Yan,et al."Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 16.5(2020):3006-3017. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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