A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 13 Issue 4
Apr.  2026

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 19.2, Top 1 (SCI Q1)
    CiteScore: 28.2, Top 1% (Q1)
    Google Scholar h5-index: 95， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(4): 986-988
Y. Hou, P. Tang, and X. Luo, “Multi-aspect self-attending neural Tucker factorization for spatiotemporal representation learning,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 986–988, Apr. 2026. doi: 10.1109/JAS.2025.125723
Citation: Y. Hou, P. Tang, and X. Luo, “Multi-aspect self-attending neural Tucker factorization for spatiotemporal representation learning,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 986–988, Apr. 2026. doi: 10.1109/JAS.2025.125723
shu

Multi-Aspect Self-Attending Neural Tucker Factorization for Spatiotemporal Representation Learning

doi: 10.1109/JAS.2025.125723
More Information
    Abstract
    • FullText(HTML)
  • loading
    • References(18)
  • [1]
    N. Zeng, X. Li, P. Wu, H. Li, and X. Luo, “A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 487–501, 2024. doi: 10.1109/JAS.2023.124029
    [2]
    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, vol. 11, no. 10, pp. 2188–2190, 2024. doi: 10.1109/JAS.2024.124278
    [3]
    P. Wu, H. Li, L. Hu, J. Ge, and N. Zeng, “A local-global attention fusion framework with tensor decomposition for medical diagnosis,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 6, pp. 1536–1538, 2024.
    [4]
    T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Rev., vol. 51, no. 3, pp. 455–500, 2009. doi: 10.1137/07070111X
    [5]
    T. Nguyen, T. Tran, D. Phung, and S. Venkatesh, “Tensor-variate restricted Boltzmann machines,” Proc. AAAI Conf. Artif. Intell., vol. 29, no. 1, pp. 2887–2893, 2015. doi: 10.1609/aaai.v29i1.9553
    [6]
    J. Chen, K. Liu, X. Luo, Y. Yuan, K. Sedraoui, Y. Al-Turki, and M. C. Zhou, “A state-migration particle swarm optimizer for adaptive latent factor analysis of high-dimensional and incomplete data,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 11, pp. 2220–2235, 2024. doi: 10.1109/JAS.2024.124575
    [7]
    H. Wu, X. Luo, and M. Zhou, “Neural latent factorization of tensors for dynamically weighted directed networks analysis,” in Proc. IEEE Int. Conf. Syst., Man, and Cybern., 2021, pp. 3061–3066.
    [8]
    X. Luo, Z. Liu, S. Li, M. Shang, and Z. Wang, “A fast non-negative latent factor model based on generalized momentum method,” IEEE Trans. Syst, Man, and Cybern: Syst., vol. 51, no. 1, pp. 610–620, 2021. doi: 10.1109/TSMC.2018.2875452
    [9]
    H. Wu, X. Luo, M. 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
    [10]
    X. Luo, M. Zhou, Y. Xia, and Q. Zhu, “An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems,” IEEE Trans. Ind. Inf., vol. 10, no. 2, pp. 1273–1284, 2014. doi: 10.1109/TII.2014.2308433
    [11]
    X. Luo, M. Zhou, Z. Wang, Y. Xia, and Q. Zhu, “An effective scheme for QoS estimation via alternating direction method-based matrix factorization,” IEEE Trans. Serv. Comput., vol. 12, no. 4, pp. 503–518, 2019. doi: 10.1109/TSC.2016.2597829
    [12]
    X. Luo, H. Wu, H. Yuan, and M. Zhou, “Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors,” IEEE Trans. Cybern., vol. 50, no. 5, pp. 1798–1809, 2020. doi: 10.1109/TCYB.2019.2903736
    [13]
    P. Tang and X. Luo, “Neural Tucker factorization,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 2, pp. 475–477, 2025. doi: 10.1109/JAS.2024.124977
    [14]
    X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “LightGCN: Simplifying and powering graph convolution network for recommendation,” in Proc. 43rd Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., 2020, pp. 639–648.
    [15]
    W. Zhang, H. Sun, X. Liu, and X. Guo, “Temporal QoS-aware web service recommendation via non-negative tensor factorization,” in Proc. 23rd Int. Conf. World Wide Web, 2014, pp. 585–596.
    [16]
    F. Ye, Z. Lin, C. Chen, Z. Zheng, and H. Huang, “Outlier-resilient web service QoS prediction.” in Proc. 30th Web Conf., 2021, pp. 3099–3110.
    [17]
    X. Chen and L. Sun, “Bayesian temporal factorization for multidimensional time series prediction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 9, pp. 4659–4673, 2022.
    [18]
    X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua, “Neural collaborative filtering,” in Proc. 26th Int. Conf. World Wide Web, 2017, pp. 173–182.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(2)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (18) PDF downloads(1) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return