  We develop a novel probabilistic generative model based on the variational
autoencoder approach. Notable aspects of our architecture are: a novel way of
specifying the latent variables prior, and the introduction of an ordinality
enforcing unit. We describe how to do supervised, unsupervised and
semi-supervised learning, and nominal and ordinal classification, with the
model. We analyze generative properties of the approach, and the classification
effectiveness under nominal and ordinal classification, using two benchmark
datasets. Our results show that our model can achieve comparable results with
relevant baselines in both of the classification tasks.
