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Masking effects on linear regression in multi-class classification

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  • Zhang, Chunming
  • Fu, Haoda

Abstract

The linear regression method belongs to the important class of linear methods for multi-class classification. Empirical evidences suggest that a masking problem occurs with the linear regression approach and it is especially prevalent when the number of classes is large. This paper provides an analytical study of this issue and explicitly explains why the linear discriminant analysis procedure removes this problem.

Suggested Citation

  • Zhang, Chunming & Fu, Haoda, 2006. "Masking effects on linear regression in multi-class classification," Statistics & Probability Letters, Elsevier, vol. 76(16), pages 1800-1807, October.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:16:p:1800-1807
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    Cited by:

    1. Zhang, Chunming & Fu, Haoda & Jiang, Yuan & Yu, Tao, 2007. "High-dimensional pseudo-logistic regression and classification with applications to gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 452-470, September.

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