Segmented principal component transform-partial least squares regression

被引：3
|
作者：
Barros, Antnio S. [1 ]
Pinto, Rui
Delgadillo, Ivonne
Rutledge, Douglas N.
机构：
[1] Univ Aveiro, Dept Quim, P-3810193 Aveiro, Portugal
[2] AgroParisTech, Chim Analyt Lab, F-75005 Paris, France
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2007年 / 89卷 / 02期
关键词：
PLS; cross validation; segmented PLS; principal component transform; KERNEL-PCA ALGORITHMS; PLS-REGRESSION; CROSS-VALIDATION; DATA SETS; WIDE DATA; SELECTION; CLASSIFICATION; CHEMOMETRICS;
D O I：
10.1016/j.chemolab.2007.05.009
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An approach for doing PLS on very wide datasets is proposed in this work. The method is based on the decomposition, by means of a SVD, of non-superimposed segments of the original data matrix. It is shown that this approach uses less computer resources compared to SIMPLS and PCT-PLS1. Furthermore, it is also shown that the results obtained by this approach are the same as those obtained by other regression methods (PLS and SIMPLS). The method implementation is simple and can be done in a distributed environment. (c) 2007 Elsevier B.V All rights reserved.
引用
收藏
页码：59 / 68
页数：10
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