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Elastic Net Grouping Variable Selection Combined with Partial Least Squares Regression (EN-PLSR) for the Analysis of Strongly Multi-collinear Spectroscopic Data.
- Source :
-
Applied Spectroscopy . Apr2011, Vol. 65 Issue 4, p402-408. 7p. - Publication Year :
- 2011
-
Abstract
- In this paper a novel wavelength region selection algorithm, called elastic net grouping variable selection combined with partial least squares regression (EN-PLSR), is proposed for multi-component spectral data analysis. The EN-PLSR algorithm can automatically select successive strongly correlated prediction variable groups related to the response variable using two steps. First, a portion of the correlated predictors are selected and divided into subgroups by means of the grouping effect of elastic net estimation. Then, a recursive leave-one-group-out strategy is employed to further shrink the variable groups in terms of the root mean square error of cross-validation (RMSECV) criterion. The performance of the algorithm with real near-infrared (NIR) spectroscopic data sets shows that the EN-PLSR algorithm is competitive with full-spectrum PLS and moving window partial least squares (MWPLS) regression methods and it is suitable for use with strongly correlated spectroscopic data. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00037028
- Volume :
- 65
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Applied Spectroscopy
- Publication Type :
- Academic Journal
- Accession number :
- 59332506
- Full Text :
- https://doi.org/10.1366/10-06069