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Can we beat over‒fitting?
Author(s) -
Cloarec Olivier
Publication year - 2014
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2602
Subject(s) - counterintuitive , multivariate statistics , partial least squares regression , latent variable , computation , computer science , algorithm , regression , linear regression , mathematics , artificial intelligence , statistics , machine learning , philosophy , epistemology
Over‒fitting in multivariate regression is often viewed as the consequence of the number of variables. However, it is almost counterintuitive that the number of variables used to fit a regression model increases the risk of over‒fitting instead of adding useful information. In this paper, we will be discussing the source of over‒fitting and ways of reducing it during the computation of partial least squares (PLS) components. A close look at the linear algebra used for PLS component calculation will highlight hints of the origin of over‒fitting. Simulation of multivariate datasets will explore the influence of noise, number of variables and complexity of the underlying latent variable structure on over‒fitting. A tentative solution to overcome the identified problem will be presented, and a new PLS algorithm will be proposed. Finally, the properties of this new algorithm will be explored. Copyright © 2014 JohnWiley & Sons, Ltd.