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ESANN
2007

Feature clustering and mutual information for the selection of variables in spectral data

8 years 10 months ago
Feature clustering and mutual information for the selection of variables in spectral data
Spectral data often have a large number of highly-correlated features, making feature selection both necessary and uneasy. A methodology combining hierarchical constrained clustering of spectral variables and selection of clusters by mutual information is proposed. The clustering allows reducing the number of features to be selected by grouping similar and consecutive spectral variables together, allowing an easy interpretation. The approach is applied to two datasets related to spectroscopy data from the food industry.
Catherine Krier, Damien François, Fabrice R
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Catherine Krier, Damien François, Fabrice Rossi, Michel Verleysen
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