Nonconvex <inline-formula> <tex-math notation="LaTeX">$l_p$ </tex-math></inline-formula>-Norm Regularized Sparse Self-Representation for Traffic Sensor Data Recovery

Nonconvex <inline-formula> <tex-math notation="LaTeX">$l_p$ </tex-math></inline-formula>-Norm Regularized Sparse Self-Representation for Traffic Sensor Data Recovery

Recovering missing values from incomplete traffic sensor data is an important task for intelligent transportation system because most algorithms require data with complete entries as input. Self-representation-based matrix completion attempts to optimally represent each sample by linearly combining...

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Bibliographic Details
Main Authors: Xiaobo Chen, Yingfeng Cai, Qingchao Liu, Lei Chen
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Traffic sensor data
missing values
<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">lp</italic>-norm regularization
sparse self-representation
Online Access:https://ieeexplore.ieee.org/document/8353230/

Internet

https://ieeexplore.ieee.org/document/8353230/

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