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Section: New Results

Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis

Participant : Marie Chavent.

A common approach to deal with missing values in Exploratory Data Analysis consists in minimizing the loss function over all non-missing elements. This 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 work proposes such an algorithm, named iterative MCA, to handle missing values in Multiple Correspondence Analysis (MCA). This 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 are assessed from both simulations and a real dataset. Results are promising for MAR and MCAR values with respect to other methods such as missing-data passive modified margin, an adaptation of missing passive method used in Gifi's Homogeneity analysis framework. This work is submitted [55]


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