| MLCGMM | |
Maximal Likelihood estimation of Gaussian mixture model.
Synopsis:
model = mlcgmm(X)
model = mlcgmm(X,cov_type)
model = mlcgmm(data)
model = mlcgmm(data,cov_type)
Description:
It computes Maximum Likelihood estimation of parameters
of Gaussian mixture model for given labeled data sample
(complete data).
model = mlcgmm(X) computes parameters (model.Mean,model.Cov)
of a single Gaussian distribution for given sample of column
vectors X (all labels are assumed to be 1).
model = mlcgmm(X,cov_type) specifies shape of covariance matrix:
cov_type = 'full' full covariance matrix (default)
cov_type = 'diag' diagonal covarinace matrix
cov_type = 'spherical' spherical covariance matrix
model = mlcgmm(data) computes parameters of a Gaussian mixture model
from a given labeled data sample
data.X ... samples,
data.y .. labels.
It estimates parameters of ncomp=max(data.y) Gaussians and
a priory probabilities Prior [1 x ncomp] using Maximum-Likelihood
principle.
Input:
X [dim x num_data] Data sample.
data.X [dim x num_data] Data sample.
data.y [1 x num_data] Data labels.
cov_type [string] Type of covariacne matrix (see above).
Output:
model [struct] Estimated Gaussian mixture model:
.Mean [dim x ncomp] Mean vectors.
.Cov [dim x dim x ncomp] Covariance matrices.
.Prior [1 x ncomp] Estimated a priory probabilities.
Example:
data = load('riply_trn');
model = mlcgmm( data );
figure; hold on; ppatterns(data); pgauss( model );
figure; hold on; ppatterns(data); pgmm( model );
See also
EMGMM, MMGAUSS, PDFGMM.
About: Statistical Pattern Recognition Toolbox
(C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac
Czech Technical University Prague
Faculty of Electrical Engineering
Center for Machine Perception
Modifications:
17-aug-2004, VF, labels y do not have to form a sequence 1,2,...,max_y
2-may-2004, VF
29-apr-2004, VF
19-sep-2003, VF
27-feb-2003, VF