3D visualization of GMM learning via the EM algorithm 1.0

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 each value 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 Gaussian ellipsoids is given by interpolating enough "frames" in between these anchor frames. The Gaussians and data are all colored to reflect which Gaussians are "responsible" for which points and transparency is used to show the relative importance of each Gaussian in the mixture.It should be noted that the interpolated ellipsoids are only for visualization purposes and aren't actually generated by the EM algorithm itself. They just help to get some intuition about the geometric interpretation of the algorithm.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Colored,  Ellipsoids,  Esponsible,  Frames,  Movement,  Points,  Reflect
Users rating:	0/10