A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis
文献类型：期刊论文
| 作者 | Hao Wu; Xin Luo; MengChu Zhou; Muhyaddin J. Rawa; Khaled Sedraoui; Aiiad Albeshri |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2022 |
| 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 |
| DOI | 10.1109/JAS.2021.1004308 |
| 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47213]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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