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Ridge-forward quadratic discriminant analysis in high-dimensional situations

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Abstract

Quadratic discriminant analysis is a classical and popular classification tool, but it fails to work in high-dimensional situations where the dimension p is larger than the sample size n. To address this issue, the authors propose a ridge-forward quadratic discriminant (RFQD) analysis method via screening relevant predictors in a successive manner to reduce misclassification rate. The authors use extended Bayesian information criterion to determine the final model and prove that RFQD is selection consistent. Monte Carlo simulations are conducted to examine its performance.

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Author information

Authors and Affiliations

  1. School of Statistics, East China Normal University, Shanghai, 200241, China

    Cui Xiong & Xinchao Luo

  2. College of Mathematics and Statistics, Institute of Statistical Sciences, Shen Zhen-Hong Kong Joint Research Centre for Applied Statistical Sciences, Shenzhen University, Shenzhen, 518060, China

    Jun Zhang

Authors
  1. Cui Xiong
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  2. Jun Zhang
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  3. Xinchao Luo
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Corresponding author

Correspondence to Cui Xiong.

Additional information

ZHANG Jun’s research was supported by the National Natural Science Foundation of China under Grant No. 11401391.

This paper was recommended for publication by Editor SHAO Jun.

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Xiong, C., Zhang, J. & Luo, X. Ridge-forward quadratic discriminant analysis in high-dimensional situations. J Syst Sci Complex 29, 1703–1715 (2016). https://doi.org/10.1007/s11424-016-6024-1

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  • DOI: https://doi.org/10.1007/s11424-016-6024-1

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