RECENT DEVELOPMENTS IN DISCRIMINANT-ANALYSIS ON HIGH-DIMENSIONAL SPECTRAL DATA

There are basically two strategies which can be used to discriminate h igh dimensional spectral data. It is common practice to first reduce t he dimensionality by some feature extraction preprocessing method, and then use an appropriate (low-dimensional) classifier. An alternative procedure is to use a (high-dimensional) classifier which is capable o f handling a large number of variables. We introduce some novel dimens ion reducing techniques as well as low and high dimensional classifier s which have evolved only recently. The discrete wavelet transform is introduced as a method for extracting features. The Fourier transform, principal component analysis, stepwise strategies, and other variable selection methods for reducing the dimensionality are also discussed. The low dimensional classifier, flexible discriminant analysis is a n ew method which combines nonparametric regression with Fisher's linear discriminant analysis to achieve nonlinear decision boundaries. We al so discuss some of the time honoured techniques such as Fisher's linea r discriminant analysis, and the Bayesian linear and quadratic methods . The modern high dimensional classifiers which we report on are penal ized discriminant analysis and regularized discriminant analysis. Each of the classifiers and a selection of dimensionality reducing techniq ues are applied to the discrimination of seagrass spectral data. Resul ts indicate a promising future for wavelets in discriminant analysis, and the recently introduced flexible and penalized discriminant analys is. Regularized discriminant analysis also performs well.