Deep Learning techniques for classification of data with missing values
Two deep learning techniques for classification on corrupt data are investigated and compared by performance. A simple imputation before classification is compared to imputation using a Variational Autoencoder (VAE). Both single and multiple imputation using the VAE are considered and compared in classification performance for different types and levels of corruption, and for different sample size