I used fitgmdist to make gmm with my data.
Since my model includes too many parameters(too high dimension), I got an error message that 'covariance matrix is ill-conditioned'.
And I saw the 'RegularizationValue' option in fitgmdist function. By setting Regularization value to 0.0001, I was able to make GMM.
I know that regularization value prevents one feature from blowing up or getting very small so it can be a solution for overfitting problem.
But I don't know how it works.
I mean..by adding very small positive number(i.e. 0.0001) on the diagonal of covariance matrix, how this action can make covariance matrix's condition better(positive definite)?