Handling incomplete heterogeneous data using VAEs

Highlights：

• Evidence Lower Bound on incomplete datasets, computed only on the observed data, regardless of the pattern of missing data.

• Generative model that handles mixed numerical and nominal likelihood models, parametrized using deep neural networks (DNNs).

• Stable recognition model that handles incomplete datasets without increasing its complexity or promoting overfitting.

• Data-normalization input/output layer prevents a few dimensions of the data dominating the training of the VAE, improving the training convergence.

• Comparison with state-of-the-art methods on six datasets for both missing data imputation and predictive tasks.