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Partial least squares classification for high dimensional data using the PCOUT algorithm

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  • Asuman Turkmen
  • Nedret Billor

Abstract

Classification of samples into two or multi-classes is to interest of scientists in almost every field. Traditional statistical methodology for classification does not work well when there are more variables (p) than there are samples (n) and it is highly sensitive to outlying observations. In this study, a robust partial least squares based classification method is proposed to handle data containing outliers where $$n\ll p.$$ The proposed method is applied to well-known benchmark datasets and its properties are explored by an extensive simulation study. Copyright Springer-Verlag 2013

Suggested Citation

  • Asuman Turkmen & Nedret Billor, 2013. "Partial least squares classification for high dimensional data using the PCOUT algorithm," Computational Statistics, Springer, vol. 28(2), pages 771-788, April.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:2:p:771-788
    DOI: 10.1007/s00180-012-0328-y
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    References listed on IDEAS

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