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.