This is the standard EM algorithm for GMMs, presented in Bishop's book "Pattern Recognition and Machine Learning", Chapter 9, with one small exception, the addition of a uniform distribution to the mixture to pick up background noise/speckle; data points which one would not want to associate with any cluster.
NOTE: This function requires the MATLAB Statistical Toolbox and, for plotting the ellipses, the function error_ellipse, available from http://www.mathworks.com/matlabcentral/fileexchange/4705. Also requires at least MATLAB 7.9 (2009b)
For a demo example simply run GM_EM();
Plotting is provided automatically for 1D/2D cases with 5 GMs or less.
Usage: % GM_EM - fit a Gaussian mixture model to N points located in n-dimensional space.
% GM_EM(X,k) - fit a GMM to X, where X is N x n and k is the number of
% clusters. Algorithm follows steps outlined in Bishop
% (2009) 'Pattern Recognition and Machine Learning', Chapter 9.
% Optional inputs
% bn_noise - allow for uniform background noise term ('T' or 'F',
% default 'T'). If 'T', relevant classification uses the
% (k+1)th cluster
% reps - number of repetitions with different initial conditions
% (default = 10). Note: only the best fit (in a likelihood sense) is
% max_iters - maximum iteration number for EM algorithm (default = 100)
% tol - tolerance value (default = 0.01)
% idx - classification/labelling of data in X
% mu - GM centres