  Missing data is a significant problem impacting all domains. State-of-the-art
framework for minimizing missing data bias is multiple imputation, for which
the choice of an imputation model remains nontrivial. We propose a multiple
imputation model based on overcomplete deep denoising autoencoders. Our
proposed model is capable of handling different data types, missingness
patterns, missingness proportions and distributions. Evaluation on several real
life datasets show our proposed model significantly outperforms current
state-of-the-art methods under varying conditions while simultaneously
improving end of the line analytics.
