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An Alternating-Direction-Method of Multipliers-Incorporated Approach to Symmetric Non-Negative Latent Factor Analysis

文献类型：期刊论文

作者Luo, Xin1,2; Zhong, Yurong1,2; Wang, Zidong3; Li, Maozhen4
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-11-15
页码15
关键词Symmetric matrices Computational modeling Data models Analytical models Training Learning systems Convergence Alternating-direction-method of multipliers (ADMM) learning system missing data non-negative latent factor analysis (NLFA) symmetric high-dimensional and incomplete matrix (SHDI) undirected weighted network
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3125774
通讯作者Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要Large-scale undirected weighted networks are frequently encountered in big-data-related applications concerning interactions among a large unique set of entities. Such a network can be described by a Symmetric, High-Dimensional, and Incomplete (SHDI) matrix whose symmetry and incompleteness should be addressed with care. However, existing models fail in either correctly representing its symmetry or efficiently handling its incomplete data. For addressing this critical issue, this study proposes an Alternating-Direction-Method of Multipliers (ADMM)-based Symmetric Non-negative Latent Factor Analysis (ASNL) model. It adopts fourfold ideas: 1) implementing the data density-oriented modeling for efficiently representing an SHDI matrix's incomplete and imbalanced data; 2) separating the non-negative constraints from the decision parameters to avoid truncations during the training process; 3) incorporating the ADMM principle into its learning scheme for fast model convergence; and 4) parallelizing the training process with load balance considerations for high efficiency. Empirical studies on four SHDI matrices demonstrate that ASNL significantly outperforms several state-of-the-art models in both prediction accuracy for missing data of an SHDI and computational efficiency. It is a promising model for handling large-scale undirected networks raised in real applications.
资助项目National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[61873148] ; National Natural Science Foundation of China[61933007] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Natural Science Foundation of Chongqing (China)[CAAIXSJLJJ-2020-004B] ; 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:000732232000001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/14734]  
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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Brunel Univ London, Dept Comp Sci, Uxbridge UB8 3PH, Middx, England
4.Brunel Univ London, Dept Elect & Comp Engn, Uxbridge UB8 3PH, Middx, England
推荐引用方式
GB/T 7714		 Luo, Xin,Zhong, Yurong,Wang, Zidong,et al. An Alternating-Direction-Method of Multipliers-Incorporated Approach to Symmetric Non-Negative Latent Factor Analysis[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:15.
APA Luo, Xin,Zhong, Yurong,Wang, Zidong,&Li, Maozhen.(2021).An Alternating-Direction-Method of Multipliers-Incorporated Approach to Symmetric Non-Negative Latent Factor Analysis.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,15.
MLA Luo, Xin,et al."An Alternating-Direction-Method of Multipliers-Incorporated Approach to Symmetric Non-Negative Latent Factor Analysis".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):15.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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