Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual gmminit Daniel@0: Daniel@0: Daniel@0: Daniel@0:

gmminit Daniel@0:

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Daniel@0: Purpose Daniel@0:

Daniel@0: Initialises Gaussian mixture model from data Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

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Daniel@0: mix = gmminit(mix, x, options)
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Daniel@0: Description Daniel@0:

Daniel@0: mix = gmminit(mix, x, options) uses a dataset x Daniel@0: to initialise the parameters of a Gaussian mixture Daniel@0: model defined by the data structure mix. The k-means algorithm Daniel@0: is used to determine the centres. The priors are computed from the Daniel@0: proportion of examples belonging to each cluster. Daniel@0: The covariance matrices are calculated as the sample covariance of the Daniel@0: points associated with (i.e. closest to) the corresponding centres. Daniel@0: For a mixture of PPCA model, the PPCA decomposition is calculated Daniel@0: for the points closest to a given centre. Daniel@0: This initialisation can be used as the starting point for training the Daniel@0: model using the EM algorithm. Daniel@0: Daniel@0:

Daniel@0: Example Daniel@0:

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Daniel@0: mix = gmm(3, 2);
Daniel@0: options = foptions;
Daniel@0: options(14) = 5;
Daniel@0: mix = gmminit(mix, data, options);
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Daniel@0: Daniel@0: This code sets up a Gaussian mixture model with 3 centres in 2 dimensions, and Daniel@0: then initialises the parameters from the data set data with 5 iterations Daniel@0: of the k means algorithm. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: gmm

Daniel@0: Pages: Daniel@0: Index Daniel@0:

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