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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Chen, X., Zhang, C., Zhao, X.-L., Saunier, N., & Sun, L. (2025). Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorization. Transportation Science, 18 pages. External link
Chen, X., Zhao, X.-L., & Cheng, C. (2024). Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization. INFORMS journal on computing. External link
Chen, X., Zhang, C., Chen, X., Saunier, N., & Sun, L. (2024). Discovering Dynamic Patterns from Spatiotemporal Data with Time-Varying Low-Rank Autoregression. IEEE Transactions on Knowledge and Data Engineering, 36(2), 504-517. External link
Chen, X., Cheng, Z., Cai, H.Q., Saunier, N., & Sun, L. (2024). Laplacian Convolutional Representation for Traffic Time Series Imputation. IEEE Transactions on Knowledge and Data Engineering, 3419698 (13 pages). External link
Chen, X. (2023). Matrix and Tensor Models for Spatiotemporal Traffic Data Imputation and Forecasting [Ph.D. thesis, Polytechnique Montréal]. Restricted access
Chen, X., & Sun, L. (2022). Bayesian temporal factorization for multidimensional time series prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 4659-4673. External link
Chen, X., Lei, M., Saunier, N., & Sun, L. (2021). Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation. IEEE Transactions on Intelligent Transportation Systems, 23(8), 12301-12310. External link
Chen, X., Chen, Y., Saunier, N., & Sun, L. (2021). Scalable low-rank tensor learning for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 129, 13 pages. External link
Li, B.-Z., Zhao, X.-L., Chen, X., Ding, M., & Liu, R. W. (2024). Convolutional Low-Rank Tensor Representation for Structural Missing Traffic Data Imputation. IEEE Transactions on Intelligent Transportation Systems, 3430039 (14 pages). External link
Liu, S., Zhao, X.-L., Leng, J., Li, B.-Z., Yang, J.-H., & Chen, X. (2024). Revisiting High-Order Tensor Singular Value Decomposition from Basic Element Perspective. IEEE Transactions on Signal Processing, 3454115 (15 pages). External link
Li, B.-Z., Zhao, X.-L., Zhang, X., Ji, T.-Y., Chen, X., & Ng, M. K. (2023). A Learnable Group-Tube Transform Induced Tensor Nuclear Norm and Its Application for Tensor Completion. Siam Journal on Imaging Sciences, 16(3), 1370-1397. External link
Sun, L., Chen, X., He, Z., & Miranda-Moreno, L. F. (2023). Routine Pattern Discovery and Anomaly Detection in Individual Travel Behavior. Networks and Spatial Economics, 23(2), 407-428. External link
