COMPARISON OF REGULARIZED DISCRIMINANT-ANALYSIS, LINEAR DISCRIMINANT-ANALYSIS AND QUADRATIC DISCRIMINANT-ANALYSIS, APPLIED TO NIR DATA

Three classifiers, namely linear discriminant analysis (LDA), quadrati c discriminant analysis (QDA) and regularized discriminant analysis (R DA) are considered in this study for classification bases on NIR data. Because NIR data sets are severely ill-conditioned, the three methods cannot be directly applied. A feature selection method was used to re duce the data dimensionality, and the selected features were used as t he input of the classifiers. RDA can be considered as an intermediate method between LDA and QDA, and in several cases, RDA reduces to eithe r LDA or QDA depending on which is better. In some other cases, RDA is somewhat better. However, optimization is time consuming. It is there fore concluded that in many cases, LDA or QDA should be recommended fo r practical use, depending on the characteristics of the data. However , in those cases where even small gains in classification quality are important, the application of RDA might be useful.