Usage

1. You can select Gaussian Mixture Fitting by choosing "GaussMix", or Multiple Line Fitting by choosing "LineMix".
2. Click in a image plane to add sample points.
3. You can also add uniformly distributed sample points by clicking "RandomPts".
4. "ClearPts" enables to clear all sample points
5. You can select the number of kernels by choosing 1 to 10.
6. You can initialize the kernels by clicking "InitKernels".
7. You can start EM algorithm by selecting "EM Run" or "EM 1 Step"(one step)
8. You can stop "EM Run" by selecting "EM Stop".

Bugs
If Gaussians or Lines disappear by EM, initialize kernels by click "InitKernels".

References

• Notes on parameter estimation, expectation maximization and mixture modeling and the corresponding Java applets and activities.
• Genetic Algorithms and Mixture Modeling
• Lecture notes on Statistical Methods in Neuroimaging
• Web-based executable Applet: http://socr.ucla.edu/htmls/SOCR_Modeler.html
• SOCR EM Wiki activities
• HTML JavaDocs
• Source code
  • http://socr.googlecode.com/
  • http://code.google.com/p/socr/source/browse/#svn/trunk/SOCR2.0/src/edu/ucla/stat/SOCR
  • http://code.google.com/p/socr/source/browse/trunk/SOCR2.0/src/edu/ucla/stat/SOCR/modeler/RiceFit_Modeler.java
• EBook Section on MOM and MLE estimation

Acknowledgments

• The EM mixture modeling algorithm is formally published in Neural Computation, Vol.12, No. 6, 1411-1428, 2000
  
• Dr. Dowe's page about mixture modeling, Akaho's Home Page
  

Ivo Dinov's Home

http://www.stat.ucla.edu/~dinov

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I.Dinov 1997-2009

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