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EM algorithm for Gaussian mixture model with background noise

version 1.1.0.0 (3.07 KB) by Andrew
Standard EM algorithm to fit a GMM with the (optional) consideration of background noise.

3.2K Downloads

Updated 16 May 2012

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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
% returned.
% max_iters - maximum iteration number for EM algorithm (default = 100)
% tol - tolerance value (default = 0.01)

% Outputs
% idx - classification/labelling of data in X
% mu - GM centres

Cite As

Andrew (2021). EM algorithm for Gaussian mixture model with background noise (https://www.mathworks.com/matlabcentral/fileexchange/36721-em-algorithm-for-gaussian-mixture-model-with-background-noise), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2009b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Acknowledgements

Inspired by: EM Algorithm for Gaussian Mixture Model (EM GMM)

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