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?


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.


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