GMIX_LNK(1)

NAME
gmix_lnk - Gaussian Mixture Classifier

SYNOPSIS
Program for training and testing a Gaussian mixture classifier. Each class distribution is a sum of weighted Gaussian mixture components. Each class can have its own set of Gaussian mixture components or a tied mixture model can be created, where all classes share a common set of mixture components. The number of mixture components per class is determined by a clustering program which must be run before gmix_lnk to initialize mixture components. kmeans and em_clus are two commonly used clustering programs.

gmix_lnk [ -tied ] [ -grand n ] [ -full ] [ -minvar x ]
[ -max_ratio x ] [ -var_spread x ]
[ -gmix_stop_percentage x ] [ -fclparam file ]
[ -epochs n ] [ -classifier_options ]

EXAMPLE
kmeans -train -fparam kmeans.param -ncenters 4 \
-cluster_by_class -pathdata $LNKHOME/data/class \
-fdescribe vowel.defaults -finput vowel.train \
-npatterns 338

gmix_lnk -grand 2 -fclparam kmeans.param -train -create \
-pathdata $LNKHOME/data/class -finput vowel.train \
-fdescribe vowel.defaults -npatterns 338 -epochs 10 \
-fparam gmix.param

gmix_lnk -fparam gmix.param -pathdata $LNKHOME/data/class \
-finput vowel.eval -fdescribe vowel.defaults -npatterns 166

DESCRIPTION

A Gaussian mixture classifier trains the means, variances, and connection weights of Gaussian mixture components to maximize the average likelihood of the training data in each class.

When testing, the Gaussian output of each center is found for the input vector. For each class, a weighted sum of the outputs of the centers associated with that class is calculated. This weighted sum is the likelihood of that class. The class with the highest likelihood is chosen.

OPTIONS
-- Prints a list of all flags and arguments.

-tied
    Train a set of tied mixtures. When this flag is set, one set of mixture components is trained and used by all classes.
    When this flag is not set, a separate set of mixture components trained for each class. If the centers used for initialization are trained with the cluster_by_class flag set, the clusters for each class are copied as the mixtures for that class. If the initial centers are trained ignoring class, a copy of all of the centers is made for each class. In this case there are more centers in the Gaussian mixture model than are trained by the clusterer.
    While training, the input data in each class is used for training only the centers from that class.

-grand n
    The setting for n determines the number of covariance matrices trained for a given model.

    0 - Per Center Covariances
        Train a covariance matrix for each center.

    1 - Grand Per Class Covariances
        Train a covariance matrix for each class that is used by all mixture components within a class. This flag does not apply when tied mixtures are being trained.

    2 - Grand Overall Covariances
        Train a single covariance matrix for use by all mixture components across all classes.

-full
    Train full covariance matrices. The default is to train diagonal matrices.

-minvar x
    x is the minimum value of on diagonal entries of the covariance matrices.

-max_ratio x
    x is the maximum allowable ratio of the largest eigenvalue to each of the others when doing a matrix inversion using singular value decomposition.

-var_spread x
    The variances of the initial cluster centers are multiplied by x before training begins.

-fclparam filename
    The file in pathexp with the clusters used to initialize the mixture components.

-stop_percentage x
    Stop when the average log likelihood of the data changes by less than stop_percentage from one iteration to the next.

-epochs n
    n is the number of times to run through all of the data in the input file during training. This flag has no effect when testing.

-classifier_options
    gmix_lnk accepts the general classifier arguments described on the classifier(1) manual page. Certain arguments are not used by the program, and so are not available.

    Unavailable arguments from general list:

    -ninputs -features -normalize -fnorm
        The settings for these flags are read from the cluster file used to initialize the centers.

REFERENCE
Kenney Ng, A Comparative Study of the Practical Characteristics of Neural Network and Conventional Pattern Classifiers, Bachelor's and Master's Thesis, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, May 1990. Pages 43-48.

SEE ALSO
classifier(1), gmix2c(1), gmix_feat_sel(1), plot_gmix(1), gmix_plot_bound(1), kmeans_lnk(1), em_clus_lnk(1), lead_clus_lnk(1), ran_clus_lnk(1)

HISTORY
First written 20 May 1992 by Linda Kukolich

BUGS
Send bug reports to Linda Kukolich (kukolich@sst.ll.mit.edu)