Journal
MOLECULAR INFORMATICS
Volume 31, Issue 6-7, Pages 414-419Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.201200158
Keywords
Latent variables; Predictive variation; Orthogonal variation; Interpretability
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Partial least squares (PLS) regression is a flexible data analytical approach, which can be made even more versatile and useful by various modifications. In this article we describe the extension into orthogonal PLS modeling, in terms of two new methods, called OPLS and O2PLS, with similar prediction capacity but improved model interpretation.
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