mDAE: modified Denoising AutoEncoder for missing data imputation

This paper introduces a method based on Denoising AutoEncoder (DAE) for missing data imputation.This method, called mDAE hereafter, results from a modification of the loss function and a straightforward procedure for choosing the hyper-parameters.An ablation study shows on several UCI Machine Learning Repository datasets, the benefit of using this modified loss function and an overcomplete structure, in terms of Root Mean Squared Error (RMSE) of reconstruction.This numerical study is complemented by a comparison between mDAE and eight alternative approaches (four classical and four more recent), using the Mean Distance to the Best (MDB) criterion, which quantifies the overall performance of each method across all the datasets.