Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding
This work addresses unsupervised clustering for data analysis, but it appears incremental as it combines existing methods without introducing a fundamentally new approach.
The paper tackles unsupervised clustering by integrating the Variational Information Bottleneck with a Gaussian Mixture Model in a generative framework, achieving efficiency as demonstrated by numerical results on real datasets.
In this paper, we develop an unsupervised generative clustering framework that combines the Variational Information Bottleneck and the Gaussian Mixture Model. Specifically, in our approach, we use the Variational Information Bottleneck method and model the latent space as a mixture of Gaussians. We derive a bound on the cost function of our model that generalizes the Evidence Lower Bound (ELBO) and provide a variational inference type algorithm that allows computing it. In the algorithm, the coders' mappings are parametrized using neural networks, and the bound is approximated by Monte Carlo sampling and optimized with stochastic gradient descent. Numerical results on real datasets are provided to support the efficiency of our method.