Imperial College London
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DrRuiPinto

Faculty of Medicine, School of Public Health

Research Associate in Chemometrics/Metabolomics
 
 
 
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Contact

 

+44 (0)20 7594 9761r.pinto Website

 
 
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Location

 

155Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Barros:2007:10.1016/j.chemolab.2007.05.009,
author = {Barros, AS and Pinto, R and Delgadillo, I and Rutledge, DN},
doi = {10.1016/j.chemolab.2007.05.009},
journal = {Chemometrics and Intelligent Laboratory Systems},
pages = {59--68},
title = {Segmented Principal Component Transform-Partial Least Squares regression},
url = {http://dx.doi.org/10.1016/j.chemolab.2007.05.009},
volume = {89},
year = {2007}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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. © 2007 Elsevier B.V. All rights reserved.
AU  - Barros,AS
AU  - Pinto,R
AU  - Delgadillo,I
AU  - Rutledge,DN
DO  - 10.1016/j.chemolab.2007.05.009
EP  - 68
PY  - 2007///
SN  - 0169-7439
SP  - 59
TI  - Segmented Principal Component Transform-Partial Least Squares regression
T2  - Chemometrics and Intelligent Laboratory Systems
UR  - http://dx.doi.org/10.1016/j.chemolab.2007.05.009
VL  - 89
ER  -
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