A Novel Nonconvex Low-Rank Tensor Completion Approach for Traffic Sensor Data Recovery From Incomplete Measurements-Reference-Cited by-同舟云学术

A Novel Nonconvex Low-Rank Tensor Completion Approach for Traffic Sensor Data Recovery From Incomplete Measurements

Published:2023 Issue: Volume:72 Page:1-15
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Chen Xiaobo¹^ORCID,Wang Kaiyuan¹^ORCID,Li Zuoyong²^ORCID,Zhang Yu³^ORCID,Ye Qiaolin⁴^ORCID

Affiliation:

1. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China

2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou, China

3. Department of Bioengineering, Lehigh University, Bethlehem, PA, USA

4. College of Information Science and Technology, Nanjing Forestry University, Nanjing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Taishan Scholars Program of Shandong Province

Yantai Science and Technology Development Plan

Fujian Provincial Transportation Technology Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10147850.pdf?arnumber=10147850

Reference50 articles.

1. Ensemble correlation-based low-rank matrix completion with applications to traffic data imputation

2. SGCast: A New Forecasting Framework for Multilocation Geomagnetic Data With Missing Traces Based on Matrix Factorization

3. Nonconvex $l_p$ -Norm Regularized Sparse Self-Representation for Traffic Sensor Data Recovery

4. Tensor Completion for Estimating Missing Values in Visual Data

5. A Novel Spatiotemporal Data Low-Rank Imputation Approach for Traffic Sensor Network

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