I have a huge dataset of features on which I want to fit a Gaussian Mixture Model using standard expectation maximization, as it is implemented by sklearn. Since not all features fit into the memory at once, I need to train my GMM iteratively, i.e. one batch after the other. What is the best approach to do this? Just go for GMM.fit(batch) for each batch? Maybe use a smaller number of iterations for the EM algorithm? Anymore thoughts, ideas, or even better references therefore?