LSPTD: Low-rank and spatiotemporal priors enhanced Tucker decomposition for internet traffic data imputation

Wenwu Gong; Zhejun Huang; Lili Yang

10.1109/ITSC57777.2023.10422071

2024-02-13

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

2153-0009

979-8-3503-9947-9

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

460-465

24-28 September 2023

Bilbao, Spain

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Low-rank tensor methods and their relaxation forms have performed excellently in tensor completion problems, including internet traffic data imputation. However, most are based on the unfolding matrix's nuclear norm, which inevitably destroys the traffic tensor structure and significantly suffers from computation burden. Also, few consider the intrinsic spatiotemporal features, especially for the underlying spatial similarity. This paper proposes a novel low-rank and spatiotemporal priors enhanced Tucker decomposition (called LSPTD) for internet traffic data imputation. LSPTD model exploits the spatial similarity using factor graph embedding and characterizes the temporal correlation using the Toeplitz matrix. Two easily implementable algorithms and the closed-form updating rules are designed to solve the LSPTD model. Numerical experiments on the Abilene and GE ' ANT datasets demonstrate that our proposed model is superior to the other imputation methods in terms of NMAE and computation time.

Tensors Computational modeling Spatiotemporal phenomena Numerical models Matrix decomposition Proposals Optimization

第一 ; 通讯

英语

CPCI-S

Shenzhen Science and Technology Plan platform and carrier special[ZDSYS20210623092007023]

Automation & Control Systems ; Computer Science ; Transportation

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Transportation Science & Technology

WOS:001178996700068

人工提交

Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, 518055, China

Wenwu Gong,Zhejun Huang,Lili Yang. LSPTD: Low-rank and spatiotemporal priors enhanced Tucker decomposition for internet traffic data imputation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2024:460-465.