A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
IEEE/CAA Journal of Automatica Sinica
| Citation: | H. Chen, M. Lin, J. Liu, and Z. Xu, “Scalable temporal dimension preserved tensor completion for missing traffic data imputation with orthogonal initialization,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124278
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X.Y. Chen and L.J. Sun, “Bayesian temporal factorization for multidimensional time series prediction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 9, pp. 4659–4673, 2022.
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H. Wu, X. Luo, M.C. Zhou, M. J. Rawa, K. Sedraoui, and A. Albeshri, “A PID-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 533–546, 2022. doi: 10.1109/JAS.2021.1004308
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X.Y. Chen, Z.C. He, Y.X. Chen, Y.H. Lu, and J.W. Wang, “Missing traffic data imputation and pattern discovery with a bayesian augmented tensor factorization model,” Transport. Res. Part C: Emerg. Technol., vol. 104, no. 7, pp. 66–7, 2019.
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X.Y. Chen, M.Y. Lei, N. Saunier, and L.J. Sun, “Low-rank autoregressive tensor completion for spatiotemporal traffic data imputation,” ,IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, pp. 12301–12310, 2021.
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X.Y. Chen, Y.X. Chen, N. Saunier, and L.J. Sun, “Scalable low-rank tensor learning for spatiotemporal traffic data imputation,” Transport. Res. Part C: Emerg. Technol., vol. 129, p. 103226, 2021. doi: 10.1016/j.trc.2021.103226
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L. Jin, X. Zheng, and X. Luo, “Neural dynamics for distributed collaborative control of manipulators with time delays,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 854–863, 2022. doi: 10.1109/JAS.2022.105446
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X.Y. Chen, J.M. Yang, and L.J. Sun, “A nonconvex low-rank tensor completion model for spatiotemporal traffic data imputation,” Transport. Res. Part C: Emerg. Technol., vol. 117, no. 8, p. 102673, 2020.
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J. Liu, P. Musialski, P. Wonka, and J. Ye, “Tensor completion for estimating missing values in visual data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 208–220, 2013. doi: 10.1109/TPAMI.2012.39
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