  State-of-the-art approaches for clustering high-dimensional data utilize deep
auto-encoder architectures. Many of these networks require a large number of
parameters and suffer from a lack of interpretability, due to the black-box
nature of the auto-encoders. We introduce Mixture Model Auto-Encoders
(MixMate), a novel architecture that clusters data by performing inference on a
generative model. Derived from the perspective of sparse dictionary learning
and mixture models, MixMate comprises several auto-encoders, each tasked with
reconstructing data in a distinct cluster, while enforcing sparsity in the
latent space. Through experiments on various image datasets, we show that
MixMate achieves competitive performance compared to state-of-the-art deep
clustering algorithms, while using orders of magnitude fewer parameters.
