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CSpace
A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis
Wu, Hao1,2; Luo, Xin1,2; Zhou, MengChu3; Rawa, Muhyaddin J.4,5; Sedraoui, Khaled6; Albeshri, Aiiad7
2022-03-01
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.
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
DOI10.1109/JAS.2021.1004308
发表期刊IEEE-CAA JOURNAL OF AUTOMATICA SINICA
ISSN2329-9266
卷号9期号:3页码:533-546
通讯作者Luo, Xin(luoxin211@cigit.ac.cn) ; Zhou, MengChu(zhou@njit.edu)
收录类别SCI
WOS记录号WOS:000735515700015
语种英语