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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis
Hao Wu; Xin Luo; MengChu Zhou; Muhyaddin J. Rawa; Khaled Sedraoui; Aiiad Albeshri
Source PublicationIEEE/CAA Journal of Automatica Sinica
ISSN2329-9266
2022
Volume9Issue:3Pages:533-546
AbstractA 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.
KeywordBig data high dimensional and incomplete (HDI) tensor latent factorization-of-tensors (LFT) machine learning missing data optimization proportional-integral-derivative (PID) controller
DOI10.1109/JAS.2021.1004308
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47213
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
Recommended Citation
GB/T 7714
Hao Wu,Xin Luo,MengChu Zhou,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 Hao Wu,Xin Luo,MengChu Zhou,Muhyaddin J. Rawa,Khaled Sedraoui,&Aiiad Albeshri.(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 Hao Wu,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.
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