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

Asuman Turkmen () and Nedret Billor ()

Computational Statistics, 2013, vol. 28, issue 2, 788 pages

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

Keywords: Partial least squares; Classification; Outlier; PCOUT; Robustness (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s00180-012-0328-y

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