How to create a multivariate gaussian mixture model??

 [counts,binLocations] = imhist(X);
        stem(binLocations, counts, 'MarkerSize', 1 );
        xlim([-1 1]); 
        % inital kmeans step used to initialize EM
        K = 2;               % number of mixtures/clusters
        rng('default');
        cInd = kmeans(X(:), K,'MaxIter', 75536);
        % fit a GMM model
        options = statset('MaxIter', 75536); 
        gmm = fitgmdist(X(:), K,'Start',cInd,'CovarianceType','diagonal','Regularize',1e-5,'Options',options);

The piece of code shows how to fit a GMM to a univariate Gaussian distribution. X is and image. But how this can be extended to create a a 2 component 2 dimensional multivariate GMM?

[Sergio Cypress]

http://web.eecs.umich.edu/~cscott/pubs/tcem_tr.pdf this paper present EM algorithm to handle Multi GMM.

Answer 1

[Prashant Arora]

Hi Akshara,

The gmdistribution function supports multivariate gaussian distributions. Check the required dimensions of mu and sigma to create a multivariate 2 dimensional 2 component distribution.