A Comparison of Two Discriminant Functions for Classifying Binary Infrared Data

Abstract

Some form of information compression is essential if one is to be able to utilize effectively the increasingly large data compilations. One approach is to eliminate the intensity information, leaving spectra packed in a peak/no peak format. This paper reports the comparison of two simple discriminant functions for classifying binary infrared data. For the multicategory problem of 13 classes used in this investigation, random guessing would achieve about 8% correct classification. A dot product calculation produces 49.1% correct classification, while a distance measurement produces 58.7%. The results from this investigation are also qualitatively compared to previous work using infrared data which retained some intensity information. It is found that the binary packing of spectral data shows great promise in the area of infrared analysis.