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Variational Dirichlet Process
Gaussian Mixture Model

Description

This software implements several algorithms. (see References.)

Algorithms

  1. accelerated variational Dirichlet process Gaussian mixture model
  2. collapsed variational stick-breaking Dirichlet process Gaussian mixture model
  3. variational Gaussian mixture model with a collapsed Dirichlet prior.
  4. variational Dirichlet process Gaussian mixture model by Blei and Jordan.
Note more algorithms are actually available. They are specified by options.

Usage

This software requires Matlab.

The usage is like,

>> result = vdpgm(X, opts);
    
The first argument is data. Each data point is a column vector of X.
The second argument opts is the option of this program which determines an algorithm and hyperparameters. You can set opts as you want, or basic option generators are also available.
>> opts = mkopts_avdp;     % for the algorithm 1
>> opts = mkopts_csb(10);  % for the algorithm 2 with T=10 
>> opts = mkopts_cdp(10);  % for the algorithm 3 with K=10 
>> opts = mkopts_bj(10);   % for the algorithm 4 with T=10 
  
Although opts accepts many options, some options are exclusive.

The output result is a structure containing parameters for posteriors.
Maybe, the most useful result is result.q_of_z which is the posterior probability of assignments. q_of_z is a N by K (or T) matrix s.t. sum_c q_of_z(i,c) = 1 for any c. q_of_z is available only when opts.get_q_of_z is set to 1.

Download

License

This software is distributed under the BSD license.
Copyright (C) 2007 Kenichi Kurihara

Source

vdpgm.tar.gz

ChangeLog

  • Aug. 24 2007 : kd-tree code had a bug; fixed.
  • Apr. 20 2007 : The previous package did not contain "power_method.m".

References

  • Kenichi Kurihara, Max Welling and Yee Whye Teh,
    Collapsed Variational Dirichlet Process Mixture Models,
    the Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007). PDF
  • Kenichi Kurihara, Max Welling and Nikos Vlassis,
    Accelerated Variational Dirichlet Mixture Models,
    Advances in Neural Information Processing Systems 19 (NIPS 2006). PDF
  • David M. Blei and Michael I. Jordan,
    Variational Inference for Dirichlet Process Mixtures,
    Bayesian Analysis, Vol.1, No.1, 2005.

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