wolffd@0: wolffd@0: wolffd@0: wolffd@0: Netlab Reference Manual gtmem wolffd@0: wolffd@0: wolffd@0: wolffd@0:

gtmem wolffd@0:

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wolffd@0: Purpose wolffd@0:

wolffd@0: EM algorithm for Generative Topographic Mapping. wolffd@0: wolffd@0:

wolffd@0: Synopsis wolffd@0:

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wolffd@0: 
wolffd@0: [net, options, errlog] = gtmem(net, t, options)
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wolffd@0: Description wolffd@0:

wolffd@0: [net, options, errlog] = gtmem(net, t, options) uses the Expectation wolffd@0: Maximization algorithm to estimate the parameters of wolffd@0: a GTM defined by a data structure net. wolffd@0: The matrix t represents the data whose expectation wolffd@0: is maximized, with each row corresponding to a vector. It is assumed wolffd@0: that the latent data net.X has been set following a call to wolffd@0: gtminit, for example. wolffd@0: wolffd@0: The optional parameters have the following interpretations. wolffd@0: wolffd@0:

options(1) is set to 1 to display error values; also logs error wolffd@0: values in the return argument errlog. wolffd@0: If options(1) is set to 0, wolffd@0: then only warning messages are displayed. If options(1) is -1, wolffd@0: then nothing is displayed. wolffd@0: wolffd@0:

options(3) is a measure of the absolute precision required of the error wolffd@0: function at the solution. If the change in log likelihood between two steps of wolffd@0: the EM algorithm is less than this value, then the function terminates. wolffd@0: wolffd@0:

options(14) is the maximum number of iterations; default 100. wolffd@0: wolffd@0:

The optional return value options contains the final error value wolffd@0: (i.e. data log likelihood) in wolffd@0: options(8). wolffd@0: wolffd@0:

wolffd@0: Examples wolffd@0:

wolffd@0: The following code fragment sets up a GTM, initialises wolffd@0: the latent data sample and RBF wolffd@0: parameters from the data, sets the options and trains the model. wolffd@0:

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wolffd@0: % Create and initialise GTM model
wolffd@0: net = gtm(latentdim, nlatent, datadim, numrbfcentres, ...
wolffd@0:    'gaussian', 0.1);
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wolffd@0: options = foptions;
wolffd@0: options(1) = -1;
wolffd@0: options(7) = 1;    % Set width factor of RBF
wolffd@0: net = gtminit(net, options, data, 'regular', latentshape, [4 4]);
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options = foptions;
wolffd@0: options(14) = 30;
wolffd@0: options(1) = 1;
wolffd@0: [net, options] = gtmem(net, data, options);
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wolffd@0: See Also wolffd@0:

wolffd@0: gtm, gtminit

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

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