3D visualization of GMM learning via the EM algorithm
by Johannes
10 Jan 2012
(Updated 04 Mar 2013)
The evolution of a GMM in the EM algorithm is visualized by interpolating between iterations.
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| File Information |
| Description |
This is a 3D visualization of how the Expectation Maximization algorithm learns a Gaussian Mixture Model for 3-dimensional data.
--How it works--
The data is either read in or generated in general-covariance gaussian clusters. For desired values of k (number of Gaussians to fit), a movie is played showing the evolution of the GMM through the iterations of the EM algorithm. The true model is only available at each iteration (viewed as an anchor frame in a movie), so the illusion of movement of the 3D Gaussians (displayed as ellipsoids at 1 standard deviation) is given by interpolating enough "frames" in between these anchor frames. |
| Required Products |
MATLAB
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| MATLAB release |
MATLAB 7.14 (R2012a)
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| Updates |
| 10 Jan 2012 |
color gradient added to data points to show the certainty that it belongs to the nearest Gaussian |
| 10 Jan 2012 |
updated description |
| 13 Jan 2012 |
reduced overhead |
| 15 Jan 2012 |
changed mean initialization from 'at data mean with small noise' to 'at data points chosen at random' (usually gives better clustering) |
| 28 Mar 2012 |
Corrected bug in EM implementation |
| 04 Mar 2013 |
Cleaned up code, improved plotting, coloring, added full-covariance capabilities |
| 04 Mar 2013 |
Simplified 2D plotting option, stream-lined code |
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