How can I find mean and covariance after EM iteration on GMM algorithmm?

Question

I have a dataset divided in 2 class(lets call x1,x2) but I don't know their mean and covariance.

For each class I looked their graph and made a guess about their sub-classes, then run an EM(Expectation-Maximization) algorithm to them one by one to find these inner classes mean and covairance. So now i have something like this: if sub-class k= 2 for x1

• x1_1mean
• x1_2mean
• x1_1cov
• x1_2cov
• x1_1alfa
• x1_2alfa

After that point how can I calculate the actual class mean and covariance from these sub-classes to test its performance on dataset?

Answer 1

Pattern Recognition and Machine Learning by Bishop, gives detailed steps for implementing EM for Gaussian mixture models.

Using the steps given here, it should be easy to implement.