A non-linear nested partial least-squares algorithm

被引：12
|
作者：
Li, BB [1 ]
Hassel, PA [1 ]
Morris, AJ [1 ]
Martin, EB [1 ]
机构：
[1] Univ Newcastle Upon Tyne, Sch Chem Engn & Adv Mat, Ctr Proc Analyt & Control Technol, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2005年 / 48卷 / 01期
关键词：
modelling; multicollinearity; NIR; non-linear PLS;
D O I：
10.1016/j.csda.2003.10.015
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A nested partial least-squares (PLS) algorithm is proposed for the modelling of non-linear systems in the presence of multicollinearity. The nested algorithm comprises both an inner and outer PLS algorithm. The objective of the outer algorithm is to extract those latent variables that will form the basis of the final application whilst the role of the inner algorithm is to derive the weight vectors for the outer PLS algorithm. Wold's non-linear PLS algorithm and the error-based weight updating procedure are special cases. The nested PLS algorithm is illustrated by application to simulated data and an industrial NIR spectral data set. (C) 2003 Elsevier B.V. All rights reserved.
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页码：87 / 101
页数：15
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