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Ensemble correlation-based low-rank matrix completion with applications to traffic data imputation
Chen, Xiaobo; Wei, Zhongjie; Li, Zuoyong; Liang, Jun; Cai, Yingfeng; Zhang, Bob
2017-09-15
Source PublicationKNOWLEDGE-BASED SYSTEMS
ISSN0950-7051
Volume132Pages:249-262
AbstractLow-rank matrix completion (LRMC) is a recently emerging technique which has achieved promising performance in many real-world applications, such as traffic data imputation. In order to estimate missing values, the current LRMC based methods optimize the rank of the matrix comprising the whole traffic data, potentially assuming that all traffic data is equally important. As a result, it puts more emphasis on the commonality of traffic data while ignoring its subtle but crucial difference due to different locations of loop detectors as well as dates of sampling. To handle this problem and further improve imputation performance, a novel correlation-based LRMC method is proposed in this paper. Firstly, LRMC is applied to get initial estimations of missing values. Then, a distance matrix containing pairwise distance between samples is built based on a weighted Pearson's correlation which strikes a balance between observed values and imputed values. For a specific sample, its most similar samples based on the distance matrix constructed are chosen by using an adaptive K-nearest neighboring (KNN) search. LRMC is then applied on these samples with much stronger correlation to obtain refined estimations of missing values. Finally, we also propose a simple but effective ensemble learning strategy to integrate multiple imputed values for a specific sample for further improving imputation performance. Extensive numerical experiments are performed on both traffic flow volume data as well as standard benchmark datasets. The results confirm that the proposed correlation-based LRMC and its ensemble learning version achieve better imputation performance than competing methods. (C) 2017 Elsevier B.V. All rights reserved.
KeywordMissing data Low-rank matrix completion Nearest neighbor Pearson's correlation Ensemble learning
DOI10.1016/j.knosys.2017.06.010
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000407184900021
PublisherELSEVIER SCIENCE BV
The Source to ArticleWOS
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被引频次[WOS]:8   [WOS记录]     [WOS相关记录]
Document TypeJournal article
专题University of Macau
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Chen, Xiaobo,Wei, Zhongjie,Li, Zuoyong,et al. Ensemble correlation-based low-rank matrix completion with applications to traffic data imputation[J]. KNOWLEDGE-BASED SYSTEMS,2017,132:249-262.
APA Chen, Xiaobo,Wei, Zhongjie,Li, Zuoyong,Liang, Jun,Cai, Yingfeng,&Zhang, Bob.(2017).Ensemble correlation-based low-rank matrix completion with applications to traffic data imputation.KNOWLEDGE-BASED SYSTEMS,132,249-262.
MLA Chen, Xiaobo,et al."Ensemble correlation-based low-rank matrix completion with applications to traffic data imputation".KNOWLEDGE-BASED SYSTEMS 132(2017):249-262.
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