Gaussian Mixture Modeling GUI (GMM DEMO) 1.0

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
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