Non-symmetrical factorial discriminant analysis for symbolic objects

In this paper we propose a generalization of the factorial discriminant analysis (FDA) to complex data structures named Symbolic Objects. We assume that the a priori classes are defined by an equal number of intention symbolic objects. The paper proposes a three-step discrimination procedure. Symbolic data are coded in suitable numerical matrices, coded variables are transformed into canonical variables, symbolic objects are visualized building maximum covering area rectangles, with respect to the canonical variables. Referring to the graphical representation, geometrical rules are proposed in order to assign new objects to a a priori class on the basis of proximity measures.