When using the EM algorithm in Gaussian Mixture Models (GMM), in the E-step, we take each x set in the training dataset to calculate and update the "weight" and parameters of each Gaussian distribution of the clusters (M-step). I have read that we do this until it converges. I am a little confused here. Does that mean it loops through the whole training dataset X every time in "one step" of the EM algorithm? Or is "one step" corresponding to calculating ONE particular x set in the whole dataset and using it to update the parameters and weights of each distribution?
To make the question more clear, does each "step" in the EM algorithm in GMMs involve the whole training dataset X or does each step mean one particular x set in the training set, and we loop over the dataset one at a time to improve one at a time?
Any help will be appreciated! Thanks.