Journal of Classification

, Volume 29, Issue 1, pp 91–116 | Cite as

Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis

  • Julie JosseEmail author
  • Marie Chavent
  • Benot Liquet
  • François Husson


A common approach to deal with missing values in multivariate exploratory data analysis consists in minimizing the loss function over all non-missing elements, which can be achieved by EM-type algorithms where an iterative imputation of the missing values is performed during the estimation of the axes and components. This paper proposes such an algorithm, named iterative multiple correspondence analysis, to handle missing values in multiple correspondence analysis (MCA). The algorithm, based on an iterative PCA algorithm, is described and its properties are studied. We point out the overfitting problem and propose a regularized version of the algorithm to overcome this major issue. Finally, performances of the regularized iterative MCA algorithm (implemented in the R-package named missMDA) are assessed from both simulations and a real dataset. Results are promising with respect to other methods such as the missing-data passive modified margin method, an adaptation of the missing passive method used in Gifi’s Homogeneity analysis framework.


Multiple correspondence analysis Categorical data Missing values Imputation Regularization 


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Julie Josse
    • 1
    Email author
  • Marie Chavent
    • 2
  • Benot Liquet
    • 3
  • François Husson
    • 1
  1. 1.Agrocampus RennesRennesFrance
  2. 2.Université V. Segalen Bordeaux 2BordeauxFrance
  3. 3.Equipe Biostatistique de l’U897 INSERM ISPEDBordeauxFrance

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