DocumentCode :
2221802
Title :
Partial least squares method based on least absolute shrinkage and selection operator
Author :
Li, Cuiying ; Li, Weiguo
Author_Institution :
Sch. of Math. & Syst. Sci., Beihang Univ., Beijing, China
Volume :
4
fYear :
2010
fDate :
20-22 Aug. 2010
Abstract :
In many multivariate statistical techniques, a set of linear functions of the original variables is produced. But this kind of model derived is difficult to interpret, Such as principle component regression (PCR) and partial least squares regression (PLSR), they cannot select variables. The approach least absolute shrinkage and selection operator (LASSO) can easily produce sparse solutions and select variables during estimate parameters. This article proposes a new technique for interpretation based on these properties, it´s a combination of partial least squares (PLS) and LASSO and can easily interpret regression models. This method will be more favorable for large number of variables compared to PLS.
Keywords :
least squares approximations; parameter estimation; regression analysis; least absolute shrinkage and selection operator; linear functions; multivariate statistical techniques; partial least squares regression; principle component regression; Algorithm design and analysis; Educational institutions; Eigenvalues and eigenfunctions; Polymers; LASSO; interpretation; partial least squares;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on
Conference_Location :
Chengdu
ISSN :
2154-7491
Print_ISBN :
978-1-4244-6539-2
Type :
conf
DOI :
10.1109/ICACTE.2010.5579283
Filename :
5579283
Link To Document :
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