Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models
文献类型：期刊论文
| 作者 | Liu, Zhigang1,3,4; Luo, Xin1,4,5 ; Wang, Zidong2
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2021-04-01 |
| 卷号 | 32期号:4页码:1737-1749 |
| 关键词 | Manganese Convergence Computational modeling Learning systems Analytical models Sparse matrices Big Data Big data convergence high-dimensional and sparse (HiDS) matrix latent factor (LF) analysis learning system neural networks nonnegative LF (NLF) analysis single LF-dependent nonnegative and multiplicative update (SLF-NMU) |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2020.2990990 |
| 通讯作者 | Luo, Xin(luoxin21@cigit.ac.cn) |
| 英文摘要 | A single latent factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU) learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a high-dimensional and sparse (HiDS) matrix. However, convergence characteristics of such NLF models are never justified in theory. To address this issue, this study conducts rigorous convergence analysis for an SLF-NMU-based NLF model. The main idea is twofold: 1) proving that its learning objective keeps nonincreasing with its SLF-NMU-based learning rules via constructing specific auxiliary functions; and 2) proving that it converges to a stable equilibrium point with its SLF-NMU-based learning rules via analyzing the Karush-Kuhn-Tucker (KKT) conditions of its learning objective. Experimental results on ten HiDS matrices from real applications provide numerical evidence that indicates the correctness of the achieved proof. |
| 资助项目 | National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[61933007] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000637534200027 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/13347]  |
| 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 Engn Res Ctr Big Data Applicat Smart Ci, Chongqing 400714, Peoples R China 2.Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England 3.Chongqing Univ Posts & Telecommun, Sch Comp Sci & Technol, Chongqing 400065, Peoples R China 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 5.Hengrui Chongqing Artificial Intelligence Res Ctr, Dept Big Data Analyses Tech, Chongqing 401331, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Zhigang,Luo, Xin,Wang, Zidong. Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(4):1737-1749. |
| APA | Liu, Zhigang,Luo, Xin,&Wang, Zidong.(2021).Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(4),1737-1749. |
| MLA | Liu, Zhigang,et al."Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.4(2021):1737-1749. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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