Using singular-value decompositions to classify spatial patterns generated by a nonlinear dynamic model of the olfactory system

Freemanʹs KIII model, a nonlinear dynamic software version of the olfactory system, has been applied for classifying drugs via their preprocessed IR spectra. In this paper, singular-value decomposition (SVD), rather than Euclidean distance classifiers, are successfully introduced to analyze the generated spatial amplitude modulation patterns. Results are compared and discussed.