Data Imputation with an Autoencoder and MAGIC

Devin Eddington, Andres Felipe Duque Correa, Guy Wolf, Kevin R. Moon

Abstract: Missing data is a common problem in many applications. Imputing missing values is a challenging task, as the imputations need to be accurate and robust to avoid introducing bias in downstream analysis. In this paper, we propose an ensemble method that combines the strengths of a manifold learning-based imputation method called MAGIC and an autoencoder deep learning model. We call our method Deep MAGIC. Deep MAGIC is trained on a linear combination of the mean squared error of the original data and the mean squared error of the MAGIC-imputed data. Experimental results on three benchmark datasets show that Deep MAGIC outperforms several state-of-the-art imputation methods, demonstrating its effectiveness and robustness in handling large amounts of missing data.

Submission Type: Full Paper