High-Dimension Traffic Data Imputation Based on a Square Norm

Huachun Tan, Pengye Wang, Yuankai Wu, Jian Zhang, Bin Ran

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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%.

源语言英语
主期刊名CICTP 2016 - Green and Multimodal Transportation and Logistics - Proceedings of the 16th COTA International Conference of Transportation Professionals
编辑Ying-En Ge, Xiaokun Wang, Yu Zhang, Youfang Huang
出版商American Society of Civil Engineers (ASCE)
284-294
页数11
ISBN(电子版)9780784479896
DOI
出版状态已出版 - 2016
活动16th COTA International Conference of Transportation Professionals: Green and Multimodal Transportation and Logistics, CICTP 2016 - Shanghai, 中国
期限: 6 7月 20169 7月 2016

出版系列

姓名CICTP 2016 - Green and Multimodal Transportation and Logistics - Proceedings of the 16th COTA International Conference of Transportation Professionals

会议

会议16th COTA International Conference of Transportation Professionals: Green and Multimodal Transportation and Logistics, CICTP 2016
国家/地区中国
Shanghai
时期6/07/169/07/16

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