Join GitHub today
GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together.Sign up
Infinite Gaussian Mixture Model
Fetching latest commit…
Cannot retrieve the latest commit at this time.
|Failed to load latest commit information.|
Michael Mandel CS 4771 Final Project The Infinite Gaussian Mixture Model Prof. Tony Jebara May 5, 2005 For my final project in Tony Jebara's Machine Learning course, cs4771, I implemented Carl Rasmussen's Infinite Gaussian Mixture Model. I got it working for both univariate and multivariate data. I'd like to see what it does when presented with MFCC frames from music and audio. There were some tricky parts of implementing it, I wrote them up in a short paper describing my implementation. Since I've gotten the multivariate case working, I'll trust you to ignore all statements to the contrary in the paper. The IGMM requires Adaptive Rejection Sampling to sample the posteriors of some of its parameters, so I implemented that as well. Thanks to Siddharth Gopal for a bugfix. See also: The paper I wrote about implementing it: http://mr-pc.org/work/cs4771igmm.pdf Jacob Eisenstein's Dirichlet process mixture model, which adds some cool features to the infinite GMM. http://people.csail.mit.edu/jacobe/software.html ===================================================== In order to generate the test data used in the paper, just make this call in matlab: [Y,z] = drawGmm([-3 3], [1 10], [1 2], 500); In order to run the infinite GMM on the data for 10000 iterations, make this call: Samp = igmm_uv(Y, 10000); It's as easy as that. If you want to run the regular univariate Gibbs Sampler on the data, do this: [mu,sigSq,p,z,churn] = gibbsGmm(Y,2,0,100,2,1,2,1000); The igmm for multivariate data is in igmm_mv.m, which uses logmvbetpdf.m instead of the logbetapdf.m used by igmm_uv.m. Otherwise, both igmms are self-contained. To generate multivariate data, use e.g. S = [2 1; 1 2]; S(:,:,2) = S [Y,z] = drawGmm([3 -3; -3 3], S, [1 1], 100); Samp = igmm_mv(Y, 10000); To generate figure 3 in the paper, use the function plotAutoCov.m ===================================================== COPYRIGHT / LICENSE ===================================================== All code was written by Michael Mandel, and is copyrighted under the (lesser) GPL: Copyright (C) 2005 Michael Mandel This program is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; version 2.1 or later. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. The authors may be contacted via email at: mim at ee columbia edu