train_class_gmm — Train a Gaussian Mixture Model.
train_class_gmm trains the Gaussian Mixture Model (GMM)
GMMHandle. Before the GMM can be trained, all
training samples to be used for the training must be stored in the
read_samples_class_gmm. After the training, new training
samples can be added to the GMM and the GMM can be trained
During the training, the error that results from the GMM applied to the training vectors will be minimized with the expectation maximization (EM) algorithm.
MaxIter specifies the maximum number of iterations per
class for the EM algorithm. In practice, values between 20
and 200 should be sufficient for most problems.
Threshold specifies a threshold for the relative changes of
the error. If the relative change in error exceeds the threshold
MaxIter iterations, the algorithm will be canceled for
this class. Because the algorithm starts with the maximum specified
number of centers (parameter
create_class_gmm), in case of a premature termination the
number of centers and the error for this class will not be
optimal. In this case, a new training with different parameters
(e.g. another value for
create_class_gmm) can be tried.
ClassPriors specifies the method of calculation of the
class priors in GMM. If 'training' is specified, the priors
of the classes are taken from the proportion of the corresponding
sample data during training. If 'uniform' is specified,
the priors are set equal to
NumClasses for all classes.
Regularize is used to regularize (nearly) singular
covariance matrices during the training. A covariance matrix might
collapse to singularity if it is trained with linearly
dependent data. To avoid this, a small value specified by
Regularize is added to each main diagonal element of the
covariance matrix, which prevents this element from becoming smaller
Regularize. A recommended value for
Regularize is 0.0001. If
Regularize is set
to 0.0, no regularization is performed.
The centers are initially randomly distributed. In individual cases,
relatively high errors will result from the algorithm because the
initial random values determined by
create_class_gmm lead to local minima. In this case, a new
GMM with a different value for
RandSeed should be generated
to test whether a significantly smaller error can be obtained.
It should be noted that, depending on the number of centers, the type of covariance matrix, and the number of training samples, the training can take from a few seconds to several hours.
train_class_gmm returns in
number of centers per class that have been found to be optimal by the
EM algorithm. These values can be used as a reference in
create_class_gmm) for future GMMs.
If the number of centers found by training a new GMM on integer
training data is unexpectedly high, this might be corrected by
Randomize noise to the training data in
Iter contains the number of
performed iterations per class. If a value in
MaxIter, the training algorithm has been terminated
prematurely (see above).
This operator modifies the state of the following input parameter:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
GMMHandle(input_control, state is modified) class_gmm
Maximum number of iterations of the expectation maximization algorithm
Default value: 100
Suggested values: 10, 20, 30, 50, 100, 200
Threshold for relative change of the error for the expectation maximization algorithm to terminate.
Default value: 0.001
Suggested values: 0.001, 0.0001
Threshold >= 0.0 && Threshold <= 1.0
Mode to determine the a-priori probabilities of the classes
Default value: 'training'
List of values: 'training', 'uniform'
Regularization value for preventing covariance matrix singularity.
Default value: 0.0001
Regularize >= 0.0 && Regularize < 1.0
Number of found centers per class
Number of executed iterations per class
create_class_gmm (NumDim, NumClasses, [1,5], 'full', 'none', 0, 42,\ GMMHandle) * Add the training data read_samples_class_gmm (GMMHandle, 'samples.gsf') * Train the GMM train_class_gmm (GMMHandle, 100, 1e-4, 'training', 1e-4, Centers, Iter) * Write the Gaussian Mixture Model to file write_class_gmm (GMMHandle, 'gmmclassifier.gmm')
If the parameters are valid, the operator
returns the value 2 (H_MSG_TRUE). If necessary an exception is
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.