How to compute AIC using Gaussian Mixture Regression?

Hello everybody, How can I manually compute the AIC for a Gaussian Mixture Model, with 2 predictor variables and one response variable. The data is approximated by 3 gaussians. I use this ( estimated values already calculated):

n=size(inputVector1,1)+size(inputVector,1);
RSS=sqrt(realValues.^2-estimatedValues.^2);
K=2;
AIC=n.*ln(RSS/n)+2*K;

Can anybody tell me what is wrong? Kind regards Joaquim

[Kiran Javkar]

Your computation of RSS (Residual Sum of Squares) is incorrect. You could use something like:

res = realValues - estimatedValues
RSS = sum(res.^2)

Check the Residuals section under this link: Linear Regression

Answer 1

[Abhishek Ballaney]

https://in.mathworks.com/help/stats/tune-gaussian-mixture-models.html

https://in.mathworks.com/help/stats/clustering-using-gaussian-mixture-models.html