  Modern datasets are becoming heterogeneous. To this end, we present in this
paper Mixed-Variate Restricted Boltzmann Machines for simultaneously modelling
variables of multiple types and modalities, including binary and continuous
responses, categorical options, multicategorical choices, ordinal assessment
and category-ranked preferences. Dependency among variables is modeled using
latent binary variables, each of which can be interpreted as a particular
hidden aspect of the data. The proposed model, similar to the standard RBMs,
allows fast evaluation of the posterior for the latent variables. Hence, it is
naturally suitable for many common tasks including, but not limited to, (a) as
a pre-processing step to convert complex input data into a more convenient
vectorial representation through the latent posteriors, thereby offering a
dimensionality reduction capacity, (b) as a classifier supporting binary,
multiclass, multilabel, and label-ranking outputs, or a regression tool for
continuous outputs and (c) as a data completion tool for multimodal and
heterogeneous data. We evaluate the proposed model on a large-scale dataset
using the world opinion survey results on three tasks: feature extraction and
visualization, data completion and prediction.
