MATLAB Answers

AIC in a Gaussian Mixture Regression

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Joaquim
Joaquim on 29 Apr 2017
Answered: Adam Danz on 17 Jan 2020
Hello everybody, I am trying to fit a gaussian mixture model to a set of predictor variables. I'm not using the built-in functions of matlab. I have six predictor variables to one response value. Each predictor variable is described by 100 observations I want to determine the number of Guassians (clusters) to fit the model. My script uses first an initialization using K-means and then the EM algorithm to calculate the model parameters ( means, covariance and mixing proportions).
How can I calculate the AIC to determine the numbers of Gaussians that bettwer fit my model?
Joaquim

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Sujit Dahal
Sujit Dahal on 17 Jan 2020
Hello Joaquim,
Did you calculate the AIC for your problem. I also have similar problem to yours. If you have the solution could you please provide it.
Thank you
Sujit

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Answers (1)

Adam Danz
Adam Danz on 17 Jan 2020
This computes the AIC and the AICc using the following defined inputs.
% RSS: Vector; residual sum of squares between your data and the fit for each model
% N: number of data points
% Np: number of model parameters (must be same size as RSS)
AIC=N*(log(RSS/N)+1)+2*(Np+1);
AICc=AIC+2*(Np+1).*(Np+2)./(N-Np-2);
Based on

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