Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters

Gaussian Mixture Models (GMMs) range among the most frequently used models in machine learning. However, training large, general GMMs becomes computationally prohibitive for data sets that have many data points $N$ of high-dimensionality $D$. For GMMs with arbitrary covariances, we here derive a hig