Discriminant Analysis on Mixed Predictors

  • Rafik AbdesselamEmail author
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The processing of mixed data – both quantitative and qualitative variables – cannot be carried out as explanatory variables through a discriminant analysis method. In this work, we describe a methodology of a discriminant analysis on mixed predictors. The proposed method uses simultaneously quantitative and qualitative explanatory data with a discrimination and classification aim. It’s a classical discriminant analysis carried out on the principal factors of a Mixed Principal Component Analysis of explanatory mixed variables, i.e. both quantitative and transformed qualitative variables associate to the dummy variables. An example resulting from real data illustrates the results obtained with this method, which are also compared with those of a logistic regression model.


Discriminant Analysis Partial Little Square Qualitative Variable Partial Little Square Discriminant Analysis Multiple Correspondence Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abdesselam, R. (2006). Mixed principal component analysis. In M. Nadif & F. X. Jollois (Eds.), Actes des XIIIémes Rencontres SFC-2006 (pp. 27–31). Metz, France.Google Scholar
  2. Escofier, B., & Pagés, J. (1979). Traitement simultané de variables quantitatives et qualitatives en analyse factorielle. Cahier de l’analyse des données, 4(2), 137–146.Google Scholar
  3. Fisher, R. (1938). The statistical utilization of multiple measurements. Annals of Eugenics, VIII, 376–386.Google Scholar
  4. Geoffrey, J., & McLachlan (2005). Discriminant analysis and data statistical pattern recognition. New York: Wiley.Google Scholar
  5. Hand, D. (1981). Discrimination and classification. New York: Wiley.zbMATHGoogle Scholar
  6. Hubert, M., & Van-Driessen, K. (2004). Fast and robust discriminant analysis. Computational Statistics and Data Analysis, 45, 301–320.zbMATHCrossRefMathSciNetGoogle Scholar
  7. Lachenbruch, P. (1975). Discriminant analysis. New York: Hafner Press.zbMATHGoogle Scholar
  8. Pagés, J. (2004). Analyse factorielle de données mixtes. Revue de Statistique Appliquée, LII(4), 93–111.Google Scholar
  9. Saporta, G. (1977). Une méthode et un programme d’analyse discriminante sur variables qualitatives. Journées internationales, Analyse des données et informatique, INRIA.Google Scholar
  10. Saporta, G. (1990). Simultaneous analysis of qualitative and quantitative data. In Atti XXXV Riunione Scientifica della Societa Italiana di Statistica (pp. 63–72).Google Scholar
  11. Sjöström, M., Wold, S., & Söderström, B. (1986). PLS discrimination plots. In: E. S. Gelsema & L. N. Kanals (Eds.), Pattern recognition in practice II. Amsterdam: Elsevier.Google Scholar
  12. Tenenhaus, M. (1998). La régression PLS: Théorie et pratique. Paris: Technip.zbMATHGoogle Scholar
  13. Tomassone, R., Danzart, M., Daudin, J. J., & Masson, J. P. (1988). Discrimination et classement (172 pp.). Paris: Masson.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.ERIC EA 3038University of Lyon 2BronFrance

Personalised recommendations