  Clustering high-dimensional data, such as images or biological measurements,
is a long-standingproblem and has been studied extensively. Recently, Deep
Clustering has gained popularity due toits flexibility in fitting the specific
peculiarities of complex data. Here we introduce the Mixture-of-Experts
Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering
model.The model can learn multi-modal distributions of high-dimensional data
and use these to generaterealistic data with high efficacy and efficiency.
MoE-Sim-VAE is based on a Variational Autoencoder(VAE), where the decoder
consists of a Mixture-of-Experts (MoE) architecture. This specific architecture
allows for various modes of the data to be automatically learned by means of
the experts.Additionally, we encourage the lower dimensional latent
representation of our model to follow aGaussian mixture distribution and to
accurately represent the similarities between the data points. Weassess the
performance of our model on the MNIST benchmark data set and challenging
real-worldtasks of clustering mouse organs from single-cell RNA-sequencing
measurements and defining cellsubpopulations from mass cytometry (CyTOF)
measurements on hundreds of different datasets.MoE-Sim-VAE exhibits superior
clustering performance on all these tasks in comparison to thebaselines as well
as competitor methods.
