HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

Mutual information for the selection of relevant variables in spectrometric nonlinear modelling

Abstract : Data from spectrophotometers form vectors of a large number of exploitable variables. Building quantitative models using these variables most often requires using a smaller set of variables than the initial one. Indeed, a too large number of input variables to a model results in a too large number of parameters, leading to overfitting and poor generalization abilities. In this paper, we suggest the use of the mutual information measure to select variables from the initial set. The mutual information measures the information content in input variables with respect to the model output, without making any assumption on the model that will be used; it is thus suitable for nonlinear modelling. In addition, it leads to the selection of variables among the initial set, and not to linear or nonlinear combinations of them. Without decreasing the model performances compared to other variable projection methods, it allows therefore a greater interpretability of the results.
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/inria-00174077
Contributor : Fabrice Rossi Connect in order to contact the contributor
Submitted on : Friday, September 21, 2007 - 2:36:40 PM
Last modification on : Thursday, February 3, 2022 - 11:14:28 AM
Long-term archiving on: : Thursday, April 8, 2010 - 8:51:19 PM

Files

Chemometrics04_Rossi-Verleysen...
Files produced by the author(s)

Identifiers

Collections

Citation

Fabrice Rossi, Amaury Lendasse, Damien François, Vincent Wertz, Michel Verleysen. Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Chemometrics and Intelligent Laboratory Systems, Elsevier, 2006, 80 (2), pp.215-226. ⟨10.1016/j.chemolab.2005.06.010⟩. ⟨inria-00174077⟩

Share

Metrics

Record views

244

Files downloads

375