Operator Reference
create_class_gmm (Operator)
create_class_gmm — Create a Gaussian Mixture Model for classification
Signature
create_class_gmm( : : NumDim, NumClasses, NumCenters, CovarType, Preprocessing, NumComponents, RandSeed : GMMHandle)
Description
create_class_gmm creates a Gaussian Mixture Model (GMM) for
classification. NumDim specifies the number of dimensions
of the feature space, NumClasses specifies the number of
classes. A GMM consists of NumCenters Gaussian
centers per class. NumCenters can not only be the exact
number of centers to be used, but, depending on the number of
parameters, can specify upper and lower bounds for the number of
centers:
- exactly one parameter:
The parameter determines the exact number of centers to be used for all classes.
- exactly two parameters:
The first parameter determines the minimum number of centers, the second determines the maximum number of centers for all classes.
- exactly parameters:
Alternatingly every first parameter determines the minimum number of centers per class and every second parameters determines the maximum number of centers per class.
When upper and lower bounds are specified, the optimum number of centers will be determined with the help of the Minimum Message Length Criterion (MML). In general, we recommend to start the training with (too) many centers as maximum and the expected number of centers as minimum.
Each center is described by the parameters center , covariance matrix , and mixing
coefficient . These parameters are calculated from
the training data by means of the Expectation Maximization (EM)
algorithm. A GMM can approximate an arbitrary probability density,
provided that enough centers are being used. The covariance matrices
have the dimensions NumDim
x NumDim (NumComponents
x NumComponents if preprocessing is used) and
are symmetric. Further constraints can be given by
CovarType:
For CovarType = 'spherical', is a scalar multiple of the identity matrix
.
The center density function is
For CovarType = 'diag',
is a diagonal matrix
.
The center density function is
For CovarType = 'full',
is a positive definite matrix. The center density function
is
The complexity of the calculations increases from CovarType
= 'spherical' over CovarType = 'diag'
to CovarType = 'full'. At the same time the
flexibility of the centers increases. In general,
'spherical' therefore needs higher values for
NumCenters than 'full'.
The procedure to use GMM is as follows: First, a GMM is created by
create_class_gmm. Then, training vectors are added by
add_sample_class_gmm, afterwards they can be written to disk
with write_samples_class_gmm. With train_class_gmm
the classifier center parameters (defined above) are determined.
Furthermore, they can be saved with write_class_gmm for
later classifications.
From the mixing probabilities and the center density function , the probability density function p(x) can be calculated by:
The probability density function p(x) can be
evaluated with evaluate_class_gmm for a feature
vector x. classify_class_gmm sorts the
p(x) and therefore discovers the most probable
class of the feature vector.
The parameters Preprocessing and NumComponents can
be used to preprocess the training data and reduce its dimensions.
These parameters are explained in the description of the operator
create_class_mlp.
create_class_gmm initializes the coordinates of the centers
with random numbers. To ensure that the results of training the
classifier with train_class_gmm are reproducible, the seed
value of the random number generator is passed in
RandSeed.
Execution Information
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Parameters
NumDim (input_control) integer → (integer)
Number of dimensions of the feature space.
Default: 3
Suggested values: 1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100
Restriction:
NumDim >= 1
NumClasses (input_control) integer → (integer)
Number of classes of the GMM.
Default: 5
Suggested values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
Restriction:
NumClasses >= 1
NumCenters (input_control) integer(-array) → (integer)
Number of centers per class.
Default: 1
Suggested values: 1, 2, 3, 4, 5, 8, 10, 15, 20, 30
Restriction:
NumClasses >= 1
CovarType (input_control) string → (string)
Type of the covariance matrices.
Default: 'spherical'
List of values: 'diag', 'full', 'spherical'
Preprocessing (input_control) string → (string)
Type of preprocessing used to transform the feature vectors.
Default: 'normalization'
List of values: 'canonical_variates', 'none', 'normalization', 'principal_components'
NumComponents (input_control) integer → (integer)
Preprocessing parameter: Number of transformed
features (ignored for Preprocessing
= 'none' and Preprocessing
= 'normalization').
Default: 10
Suggested values: 1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100
Restriction:
NumComponents >= 1
RandSeed (input_control) integer → (integer)
Seed value of the random number generator that is used to initialize the GMM with random values.
Default: 42
GMMHandle (output_control) class_gmm → (handle)
GMM handle.
Example (HDevelop)
* Classification with Gaussian Mixture Models
create_class_gmm (NumDim , NumClasses, [1,5], 'full', 'none',\
NumComponents, 42, GMMHandle)
* Add the training data
for J := 0 to NumData-1 by 1
* Features := [...]
* ClassID := [...]
add_sample_class_gmm (GMMHandle, Features, ClassID, Randomize)
endfor
* Train the GMM
train_class_gmm (GMMHandle, 100, 0.001, 'training', 0.0001, Centers, Iter)
* Classify unknown data in 'Features'
classify_class_gmm (GMMHandle, Features, 1, ID, Prob, Density, KSigmaProb)
Result
If the parameters are valid, the operator create_class_gmm
returns the value 2 (
H_MSG_TRUE)
. If necessary an exception is
raised.
Possible Successors
add_sample_class_gmm,
add_samples_image_class_gmm
Alternatives
create_class_mlp,
create_class_svm
See also
clear_class_gmm,
train_class_gmm,
classify_class_gmm,
evaluate_class_gmm,
classify_image_class_gmm
References
Christopher M. Bishop: “Neural Networks for Pattern Recognition”;
Oxford University Press, Oxford; 1995.
Mario A.T. Figueiredo: “Unsupervised Learning of Finite Mixture
Models”; IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 24, No. 3; March 2002.
Module
Foundation