Chapter
Jun 29, 2016
High-Dimension Traffic Data Imputation Based on a Square Norm
Authors: Huachun Tan [email protected], Pengye Wang [email protected], Yuankai Wu [email protected], Jian Zhang [email protected], and Bin Ran [email protected]Author Affiliations
Publication: CICTP 2016
Abstract
Traffic data is often missing or incomplete. Recently, it has been constructed into a tensor model and its missing data can be completed from a subset of its observed entries to fully express the multi-modal properties of traffic data. However, with the dimension of the data becoming higher, the completing speed, and accuracy usually decreases rapidly. To solve this problem, in this paper, we introduce a square-norm model for tensor completion, which uses the matrix completion method to accelerate the procedure of iterations, which makes it more adapted for higher-dimension tensor completion. The experimental results show that our algorithm is more suitable for those tensors with dimensions greater than three, and the accuracy can be ensured even when the missing ratio is as high as 80%.
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Published In
CICTP 2016
Pages: 284 - 294
Copyright
© 2016 American Society of Civil Engineers.
History
Published online: Jun 29, 2016
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Affiliations
Dept. of Transportation Engineering, Beijing Institute of Technology, Beijing 100084, China. E-mail: [email protected]
Dept. of Transportation Engineering, Beijing Institute of Technology, Beijing 100084, China. E-mail: [email protected]
Dept. of Transportation Engineering, Beijing Institute of Technology, Beijing, 100084, China. E-mail: [email protected]
Jiangsu Key Laboratory of Urban ITS, Southeast Univ., Jiangsu Province 210096, China. E-mail: [email protected]
Dept. of Civil and Environmental Engineering, Univ. of Wisconsin-Madison, Madison. E-mail: [email protected]
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