Handling missing values with regularized iterative multiple correspondence analysis

Julie Josse*, Marie Chavent, Benot Liquet, François Husson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)91-116
Number of pages26
JournalJournal of Classification
Issue number1
Publication statusPublished - Apr 2012
Externally publishedYes


  • Multiple correspondence analysis
  • Categorical data
  • Missing values
  • Imputation
  • Regularization


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