wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual demgmm5 wolffd@0: wolffd@0: wolffd@0: wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: Demonstrate density modelling with a PPCA mixture model. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: demgmm5

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wolffd@0: Description wolffd@0:

wolffd@0: wolffd@0: The problem consists of modelling data generated wolffd@0: by a mixture of three Gaussians in 2 dimensions with a mixture model wolffd@0: using full covariance matrices. The priors are 0.3, 0.5 and 0.2; the wolffd@0: centres are (2, 3.5), (0, 0) and (0,2); the variances are (0.16, 0.64) wolffd@0: axis aligned, (0.25, 1) rotated by 30 degrees and the identity wolffd@0: matrix. The first figure contains a scatter plot of the data. wolffd@0: wolffd@0:

A mixture model with three one-dimensional PPCA components is trained wolffd@0: using EM. The parameter vector is printed before training and after wolffd@0: training. The parameter vector consists of priors (the column), and wolffd@0: centres (given as (x, y) pairs as the next two columns). wolffd@0: wolffd@0:

The second figure is a 3 dimensional view of the density function, wolffd@0: while the third shows the axes of the 1-standard deviation ellipses wolffd@0: for the three components of the mixture model together with the one wolffd@0: standard deviation along the principal component of each mixture wolffd@0: model component. wolffd@0: wolffd@0:

wolffd@0: See Also wolffd@0:

wolffd@0: gmm, gmminit, gmmem, gmmprob, ppca

wolffd@0: Pages: wolffd@0: Index wolffd@0:

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