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Missing data is a significant problem impacting all domains. State-of-the-artframework for minimizing missing data bias is multiple imputation, for whichthe choice of an imputation model remains nontrivial. We propose a multipleimputation model based on overcomplete deep denoising autoencoders. Ourproposed model is capable of handling different data types, missingnesspatterns, missingness proportions and distributions. Evaluation on several reallife datasets show our proposed model significantly outperforms currentstate-of-the-art methods under varying conditions while simultaneouslyimproving end of the line analytics.

MIDA: Multiple Imputation using Denoising Autoencoders