中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 重庆绿色智能技术研究院
  3. 中国科学院重庆绿色智能技术研究院
A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

文献类型：期刊论文

作者Wu, Hao1,2; Luo, Xin1,2; Zhou, MengChu3; Rawa, Muhyaddin J.4,5; Sedraoui, Khaled6; Albeshri, Aiiad7
刊名IEEE-CAA JOURNAL OF AUTOMATICA SINICA
出版日期2022-03-01
卷号9期号:3页码:533-546
关键词Big data high dimensional and incomplete (HDI) tensor latent factorization-of-tensors (LFT) machine learning missing data optimization proportional-integral-derivative (PID) controller
ISSN号2329-9266
DOI10.1109/JAS.2021.1004308
通讯作者Luo, Xin(luoxin211@cigit.ac.cn) ; Zhou, MengChu(zhou@njit.edu)
英文摘要A large-scale dynamically weighted directed network (DWDN) involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications, like in a terminal interaction pattern analysis system (TIPAS). It can be represented by a high-dimensional and incomplete (HDI) tensor whose entries are mostly unknown. Yet such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN. A latent factorization-of-tensors (LFT) model proves to be highly efficient in extracting such knowledge from an HDI tensor, which is commonly achieved via a stochastic gradient descent (SGD) solver. However, an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale DWDNs. To address this issue, this work proposes a proportional-integral-derivative (PID)-incorporated LFT model. It constructs an adjusted instance error based on the PID control principle, and then substitutes it into an SGD solver to improve the convergence rate. Empirical studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models, the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.
资助项目National Natural Science Foundation of China[61772493] ; CAAI-Huawei MindSpore Open Fund[CAAIXSJLJJ-2020-004B] ; Natural Science Foundation of Chongqing of China[cstc2019jcyjjqX0013] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia[FP-165-43]
WOS研究方向Automation & Control Systems
语种英语
WOS记录号WOS:000735515700015
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/14942]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Luo, Xin; Zhou, MengChu
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA
4.King Abdulaziz Univ, Ctr Res Excellence Renewable Energy & Power Syst, Dept Elect & Comp Engn, Fac Engn, Jeddah 21589, Saudi Arabia
5.King Abdulaziz Univ, KA CARE Energy Res & Innovat Ctr, Jeddah 21589, Saudi Arabia
6.King Abdulaziz Univ, Coll Engn, Jeddah 21589, Saudi Arabia
7.King Abdulaziz Univ, Dept Comp Sci, Jeddah 21481, Saudi Arabia
推荐引用方式
GB/T 7714		 Wu, Hao,Luo, Xin,Zhou, MengChu,et al. A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis[J]. IEEE-CAA JOURNAL OF AUTOMATICA SINICA,2022,9(3):533-546.
APA Wu, Hao,Luo, Xin,Zhou, MengChu,Rawa, Muhyaddin J.,Sedraoui, Khaled,&Albeshri, Aiiad.(2022).A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis.IEEE-CAA JOURNAL OF AUTOMATICA SINICA,9(3),533-546.
MLA Wu, Hao,et al."A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis".IEEE-CAA JOURNAL OF AUTOMATICA SINICA 9.3(2022):533-546.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。