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Trainable mapping, mixture of Gaussians (MoG) density estimate

   W = GAUSSM(A,K,R,S,M)
   W = A*GAUSSM([],K,R,S,M)
   W = A*GAUSSM(K,R,S,M)

 A Dataset
 K Number of Gaussians per class
 R,S,M Regularisation parameters, 0 <= R,S <= 1, see QDC

 W Mixture of Gaussians density estimate


Estimation of a PDF by the dataset A by a mixture of Gaussians procedure.  Use is made of EMCLUST(A,QDC,K). Unlabeled objects are neglected, unless  A is entirely unlabeled or double. Then all objects are used. If A is a  multi-class crisp labeled dataset the densities are estimated class by  class and then weighted and combined according their prior probabilities.  Use +A instead of A to obtain a single set of Gaussians. In all cases,  just single density estimator W is returned.

Note that it is necessary to set the label type of A to soft labels  (A = LABTYPE(A,'soft') in order to use the traditional EM algorithm  based on posterior probabilities instead of using crisp labels.

The mapping W may be applied to a new dataset B using DENSITY = B*W.


uses a single Gaussian per class (K=1) and no regularisation. If  regulariisation is desired, also K should be supplied.


 a = gendatb;
 w = a*gaussm(2);

See also

datasets, mappings, qdc, mogc, emclust, plotm, testc,

PRTools Contents

PRTools User Guide

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