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In this paper, we develop an unsupervised generative clustering frameworkthat combines the Variational Information Bottleneck and the Gaussian MixtureModel. Specifically, in our approach, we use the Variational InformationBottleneck method and model the latent space as a mixture of Gaussians. Wederive a bound on the cost function of our model that generalizes the EvidenceLower Bound (ELBO) and provide a variational inference type algorithm thatallows computing it. In the algorithm, the coders' mappings are parametrizedusing neural networks, and the bound is approximated by Monte Carlo samplingand optimized with stochastic gradient descent. Numerical results on realdatasets are provided to support the efficiency of our method.

Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding