gmminit.htm

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<html><head><title>Netlab Reference Manual gmminit</title></head><body><H1> gmminit</H1><h2>Purpose</h2>Initialises Gaussian mixture model from data<p><h2>Synopsis</h2><PRE>mix = gmminit(mix, x, options)</PRE><p><h2>Description</h2><CODE>mix = gmminit(mix, x, options)</CODE> uses a dataset <CODE>x</CODE>to initialise the parameters of a Gaussian mixturemodel defined by the data structure <CODE>mix</CODE>.  The k-means algorithmis used to determine the centres. The priors are computed from theproportion of examples belonging to each cluster.The covariance matrices are calculated as the sample covariance of thepoints associated with (i.e. closest to) the corresponding centres.For a mixture of PPCA model, the PPCA decomposition is calculatedfor the points closest to a given centre.This initialisation can be used as the starting point for training themodel using the EM algorithm.  <p><h2>Example</h2><PRE>mix = gmm(3, 2);options = foptions;options(14) = 5;mix = gmminit(mix, data, options);</PRE>This code sets up a Gaussian mixture model with 3 centres in 2 dimensions, andthen initialises the parameters from the data set <CODE>data</CODE> with 5 iterationsof the k means algorithm.<p><h2>See Also</h2><CODE><a href="gmm.htm">gmm</a></CODE><hr><b>Pages:</b><a href="index.htm">Index</a><hr><p>Copyright (c) Ian T Nabney (1996-9)</body></html>

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