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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?

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

EM-GMM

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

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