Fast Selection of Spectral Variables with B-Spline Compression

The large number of spectral variables in most data sets encountered in spectral chemometrics often renders the prediction of a dependent variable uneasy. The number of variables hopefully can be reduced, by using either projection techniques or selection methods; the latter allow for the interpretation of the selected variables. Since the optimal approach of testing all possible subsets of variables with the prediction model is intractable, an incremental selection approach using a nonparametric statistics is a good option, as it avoids the computationally intensive use of the model itself. It has two drawbacks however: the number of groups of variables to test is still huge, and colinearities can make the results unstable. To overcome these limitations, this paper presents a method to select groups of spectral variables. It consists in a forward–backward procedure applied to the coefficients of a B-spline representation of the spectra. The criterion used in the forward–backward ...

• Type: Journal
• Year: 2007
• Where: CORR
• Authors: Fabrice Rossi, Damien François, Vincent Wertz, Marc Meurens, Michel Verleysen
• Keywords: Consecutive Spectral Variables, CORR 2007, Education, Spectral Chemometrics, Spectral Variables