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Non‐symmetrical factorial discriminant analysis for symbolic objects
Author(s) -
Palumbo Francesco,
Verde Rosanna
Publication year - 1999
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/(sici)1526-4025(199910/12)15:4<419::aid-asmb405>3.0.co;2-p
Subject(s) - symbolic data analysis , a priori and a posteriori , factorial , generalization , linear discriminant analysis , discriminant , representation (politics) , mathematics , the symbolic , basis (linear algebra) , artificial intelligence , computer science , pattern recognition (psychology) , algorithm , statistics , geometry , mathematical analysis , psychology , philosophy , epistemology , politics , political science , psychoanalysis , law
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. Copyright © 1999 John Wiley & Sons, Ltd.