classify_class_gmm — Calculate the class of a feature vector by a Gaussian Mixture Model.
classify_class_gmm computes the best Num classes of the feature vector Features with the Gaussian Mixture Model (GMM) GMMHandle and returns the classes in ClassID and the corresponding probabilities of the classes in ClassProb. Before calling classify_class_gmm, the GMM must be trained with train_class_gmm.
classify_class_gmm corresponds to a call to evaluate_class_gmm and an additional step that extracts the best Num classes. As described with evaluate_class_gmm, the output values of the GMM can be interpreted as probabilities of the occurrence of the respective classes. However, here the posterior probability ClassProb is further normalized as ClassProb = p(i|x)/p(x) , where p(i|x) and p(x) are specified with evaluate_class_gmm. In most cases it should be sufficient to use Num = 1 in order to decide whether the probability of the best class is high enough. In some applications it may be interesting to also take the second best class into account (Num = 2), particularly if it can be expected that the classes show a significant degree of overlap.
Density and KSigmaProb are explained with evaluate_class_gmm.
Number of best classes to determine.
Default value: 1
Suggested values: 1, 2, 3, 4, 5
Result of classifying the feature vector with the GMM.
A-posteriori probability of the classes.
Probability density of the feature vector.
Normalized k-sigma-probability for the feature vector.
If the parameters are valid, the operator classify_class_gmm returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.
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.