Comparing Several Methods of Discriminant Analysis on the Case of Wine Data

2000 Mathematics Subject Classification: 62H30, 62J20, 62P12, 68T99The main problem of this European wine project (WINE-DB) is the identification of the geographical origin based on chemico-analytical measurements. At first the type of data collected in preparation of this project will be analysed. Then different procedures of Discriminant analysis are described. Our special attention will be focused to some new techniques as Support Vector Mashines (also known as Kernel Mashines) - procedures from the field of Mashine Learning. We test traditional techniques of Linear, Quadratic and Nonparametric Discriminant Analysis as well as the Support Vector Mashines on the base of our data and comment the results.Partially supported by contracts: PRO-ENBIS: GTC1-2001-43031 and WINE DB: G6RDCT-2001-00646

On the Generalization of Kernel Machines

Taking advantage of the linear properties in high dimensional spaces, a general kind of kernel machines is formulated under a unified framework. These methods include KPCA, KFD and SVM. The theoretical framework will show a strong connection between KFD and SVM. The main practical result under the proposed framework is the solution of KFD for an arbitrary number of classes. The framework allows also the formulation of multiclass-SVM. The main goal of this article is focused in finding new solutions and not in the optimization of them