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T-product factorization method for internet traffic data completion with spatio-temporal regularization

Author

Listed:
  • Chen Ling

    (Hangzhou Dianzi University)

  • Gaohang Yu

    (Hangzhou Dianzi University)

  • Liqun Qi

    (Huawei Theory Research Lab
    The Hong Kong Polytechnic University)

  • Yanwei Xu

    (Huawei Theory Research Lab)

Abstract

Recovery of network traffic data from incomplete observed data is an important issue in internet engineering and management. In this paper, by fully combining the temporal stability and periodicity features in internet traffic data, a new separable optimization model for internet data recovery is proposed, which is based upon the T-product factorization and the rapid discrete Fourier transform of tensors. The separable structural features presented in the model provide the possibility to design more efficient parallel algorithms. Moreover, by using generalized inverse matrices, an easy-to-operate and effective algorithm is proposed. In theory, we prove that under suitable conditions, every accumulation point of the sequence generated by the proposed algorithm is a stationary point of the established model. Numerical simulation results carried on the widely used real-world internet network datasets, show that the proposed method outperforms state-of-the-art competitions.

Suggested Citation

  • Chen Ling & Gaohang Yu & Liqun Qi & Yanwei Xu, 2021. "T-product factorization method for internet traffic data completion with spatio-temporal regularization," Computational Optimization and Applications, Springer, vol. 80(3), pages 883-913, December.
    • Handle: RePEc:spr:coopap:v:80:y:2021:i:3:d:10.1007_s10589-021-00315-1
    DOI: 10.1007/s10589-021-00315-1
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    References listed on IDEAS

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    1. J. Carroll & Jih-Jie Chang, 1970. "Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition," Psychometrika, Springer;The Psychometric Society, vol. 35(3), pages 283-319, September.
    2. Lei Yang & Zheng-Hai Huang & Shenglong Hu & Jiye Han, 2016. "An iterative algorithm for third-order tensor multi-rank minimization," Computational Optimization and Applications, Springer, vol. 63(1), pages 169-202, January.
    3. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
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    Cited by:

    1. Quan Yu & Xinzhen Zhang, 2023. "T-product factorization based method for matrix and tensor completion problems," Computational Optimization and Applications, Springer, vol. 84(3), pages 761-788, April.

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