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A variable selection strategy for supervised classification with continuous spectroscopi data

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Kjetil Aune

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kjetil.aune@nofima.no

Journal of Chemometrics ; Volume 18. p. 53–61. 2004

Indahl, Ulf Geir; Næs, Tormod

In this paper we present a new variable selection method designed for classification problems where the X-data are discretely sampled from continuous curves. For such data, the loading weight vectors of a PLS discriminant analysis inherits the continuous behavior, making the idea of local peaks meaningful. For successive components the local peaks are checked for importance before entering the set of selected variables. Our examples with NIR/NIT show that substantial simplification of the X-space can be obtained without loss in classification power when compared to «benchmark full spectrum» methods.

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