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Gaussian Mixture Modeling GUI (GMM DEMO) 1.0

  Date Added: July 29, 2013  |  Visits: 266

Gaussian Mixture Modeling GUI (GMM DEMO)

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The Expectation-Maximization algorithm (EM) is widely used to find the parameters of a mixture of Gaussian probability density functions (pdfs) or briefly Gaussian components that fits the sample measurement vectors in maximum likelihood sense [1]. In our work, the expectation-maximization (EM) algorithm for Gaussian mixture modeling is improved via three statistical tests:a) A multivariate normality test,b) a central tendency (kurtosis) criterion, andc) a test based on marginal cdf to find a discriminant to split a non-Gaussian component.-Input ButtonsButton 1: Open Data file (.mat) or (.tif)Button 2: Draw Gaussian Data with Mouse Left mbutton = Draw Right mbutton = Jump a point Return key = Finish-Operational ButtonsButton 3: Start GMM modelingButton 4: Stop GMM modeling-Output ButtonButton 5: Save GMM parameters as a .mat fileReferences:Dimitrios Ververidis and Constantine Kotropoulos, "Gaussian mixture modeling by exploiting the Mahalanobis distance," IEEE Trans. Signal Processing, vol. 56, issue 7B, pp. 2797-2811, 2008.T. Anderson. An Introduction to Multivariate Statistical Analysis. J. Wiley & Sons: N.Y., 1984.

Requirements: No special requirements
Platforms: Matlab
Keyword: Buttonbutton Constantine Filerequirementsthe Finishoperational Introduction Kotropoulos Matlab Mbutton Modelingbutton Modelingoutput Mouse Point Return Start Tifbutton Ververidis Windows Writen Xpreferencesdimitrios
Users rating: 0/10

License: Freeware Size: 573.44 KB
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