Abstract
Routinely, the multi-response Partial Least-Squares (PLS) is used in regression and classification problems showing good performances in many applied studies. In this paper, we aim to present PLS via spline functions focusing on supervised classification studies and showing how PLS methods historically belong to L2 boosting family. The theory of the PLS boost models is presented and used in classification studies. As a natural enrichment of linear PLS boost, we present its multi-response non-linear version by univariate and bivariate spline functions to transform the predictors. Three case studies of different complexities concerning soils and its products will be discussed, showing the gain in diagnostic provided by the non-linear additive PLS boost discriminant analysis compared to the linear one.
Keywords: Partial Least-Squares regression, L2 boost, B-splines, Supervised Classification Analysis, Generalized Cross- Validation, Agro-chemical data
Current Analytical Chemistry
Title:Multivariate Additive PLS Spline Boosting in Agro-Chemistry Studies
Volume: 8 Issue: 2
Author(s): Rosaria Lombardo, Jean-Francois Durand and Antonio P. Leone
Affiliation:
Keywords: Partial Least-Squares regression, L2 boost, B-splines, Supervised Classification Analysis, Generalized Cross- Validation, Agro-chemical data
Abstract: Routinely, the multi-response Partial Least-Squares (PLS) is used in regression and classification problems showing good performances in many applied studies. In this paper, we aim to present PLS via spline functions focusing on supervised classification studies and showing how PLS methods historically belong to L2 boosting family. The theory of the PLS boost models is presented and used in classification studies. As a natural enrichment of linear PLS boost, we present its multi-response non-linear version by univariate and bivariate spline functions to transform the predictors. Three case studies of different complexities concerning soils and its products will be discussed, showing the gain in diagnostic provided by the non-linear additive PLS boost discriminant analysis compared to the linear one.
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Cite this article as:
Lombardo Rosaria, Durand Jean-Francois and P. Leone Antonio, Multivariate Additive PLS Spline Boosting in Agro-Chemistry Studies, Current Analytical Chemistry 2012; 8 (2) . https://dx.doi.org/10.2174/157341112800392661
DOI https://dx.doi.org/10.2174/157341112800392661 |
Print ISSN 1573-4110 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-6727 |
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