Articolo in rivista, 2012, ENG
Lombardo R., Durand J.F., Leone A.P.
Lombardo R. (Unina2); Durand J.F. (Montpellier University, France)
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.
Current analytical chemistry (Print) 8 (2), pp. 236–253
PLS regression, L2 boost, B-splines, Discriminant Analysis, Generalized Cross-Validation
ISAFoM – Istituto per i sistemi agricoli e forestali del mediterraneo
ID: 51883
Year: 2012
Type: Articolo in rivista
Creation: 2012-01-23 00:00:00.000
Last update: 2015-02-06 20:04:47.000
CNR authors
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:51883
ISI Web of Science (WOS): 000303474200006