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Network linear discriminant analysis

Author

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  • Cai, Wei
  • Guan, Guoyu
  • Pan, Rui
  • Zhu, Xuening
  • Wang, Hansheng

Abstract

Linear discriminant analysis (LDA) is one of the most popularly used classification methods. With the rapid advance of information technology, network data are becoming increasingly available. A novel method called network linear discriminant analysis (NLDA) is proposed to deal with the classification problem for network data. The NLDA model takes both network information and predictive variables into consideration. Theoretically, the misclassification rate is studied and an upper bound is derived under mild conditions. Furthermore, it is observed that real networks are often sparse in structure. As a result, asymptotic performance of NLDA is also obtained under certain sparsity assumptions. In order to evaluate the finite sample performance of the newly proposed methodology, a number of simulation studies are conducted. Lastly, a real data analysis about Sina Weibo is also presented for illustration purpose.

Suggested Citation

  • Cai, Wei & Guan, Guoyu & Pan, Rui & Zhu, Xuening & Wang, Hansheng, 2018. "Network linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 32-44.
    • Handle: RePEc:eee:csdana:v:117:y:2018:i:c:p:32-44
    DOI: 10.1016/j.csda.2017.07.007
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    References listed on IDEAS

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    1. Rui Pan & Hansheng Wang & Runze Li, 2016. "Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 169-179, March.
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    4. Gareth M. James & Trevor J. Hastie, 2001. "Functional linear discriminant analysis for irregularly sampled curves," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 533-550.
    5. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
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    Cited by:

    1. Li-Pang Chen & Grace Y. Yi & Qihuang Zhang & Wenqing He, 2019. "Multiclass analysis and prediction with network structured covariates," Journal of Statistical Distributions and Applications, Springer, vol. 6(1), pages 1-25, December.
    2. Li-Pang Chen, 2022. "Network-Based Discriminant Analysis for Multiclassification," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 410-431, November.

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