Gaussian Mixture Models Tutorial Slides byGaussian Mixture Models (GMMs) are among the most statistically maturemethods for clustering (though they are also used intensively fordensity estimation). In this tutorial, we introduce the concept ofclustering, and see how one form of clustering.in which we assumethat individual datapoints are generated by first choosing one of aset of multivariate Gaussians and then sampling from them.can be awell-defined computational operation. We then see how to learn such athing from data, and we discover that an optimization approach notused in any of the previous Andrew Tutorials can help considerablyhere. This optimization method is called Expectation Maximization(EM).
A Gaussian mixture model is a distribution assembled from weighted multivariate Gaussian. distributions.Weighting factors assign each distribution different levels of importance. The resulting model is a super-position (i.e. An overlapping) of bell-shaped curves. Gaussian mixture models are semi-parametric. Parametric implies that the model comes from a known distribution (which is in this. A multivariate Gaussian mixture model is used to cluster the feature data into k number of groups where k represents each state of the machine. The machine state can be.
We'll spend some time giving a few high level explanations anddemonstrations of EM, which turns out to be valuable for many otheralgorithms beyond Gaussian Mixture Models (we'll meet EM again in thelater Andrew Tutorial on Hidden Markov Models). The wild'n'crazyalgebra mentioned in the text can be found (hand-written).Powerpoint Format: The Powerpoint originals of these slides are freely available to anyonewho wishes to use them for their own work, or who wishes to teach usingthem in an academic institution. Please emailif you would like him to send them to you. The only restriction is thatthey are not freely available for use as teaching materials in classesor tutorials outside degree-granting academic institutions.Advertisment: I have recently joined Google, and am starting up the new Google Pittsburgh office on CMU's campus. We are hiring creative computer scientists who love programming, and Machine Learning is one the focus areas of the office. If you might be interested, feel welcome to send me email: [email protected].