DEVELOPMENT AND OPTIMIZATION OF PIECEWISE LINEAR DISCRIMINANTS FOR THE AUTOMATED DETECTION OF CHEMICAL-SPECIES

A pattern recognition technique based on piecewise linear discriminant analysis (PLDA) is described. Algorithms for the calculation and optimization of piecewise linear discriminants are presented. A simplex optimization of the individual discriminants is described, and a new method to optimize a piecewise linear discriminant is proposed and shown to produce significantly improved results over the nonoptimized method. This methodology is demonstrated through the use of a set of Fourier transform infrared interferograms collected by a remote sensor. The discriminant analysis methods produce a yes/no decision about the presence of a target analyte. The results obtained from the PLDA technique are compared with previous results from single linear discriminants and shown to be superior with respect to the separation statistics and the signal-to-noise ratio of the response.