gmm

Gaussian Mixture Model (GMM)

##Description The Gaussian Mixture Model (GMM) classifier is a supervised learning classification algorithm that can be used to classify a wide variety of N-dimensional signals.

The GMM algorithm is part of the GRT classification modules.

##Advantages The GMM algorithm is a good algorithm to use for the classification of static postures and non-temporal pattern recognition.

##Disadvantages The main limitation of the GMM algorithm is that, for computational reasons, it can fail to work if the dimensionality of the problem is too high (i.e. greater than 6 dimensions for instance). If this is the case with your data then you might want to try either the ANBC or Support Vector Machine classification algorithms instead.

Another disadvantage of the GMM algorithm is that the user must set the number of mixture models that the algorithm will try and fit to the training dataset. In many instances the user will not know how many mixture models should be used and may have to experiment with a number of different mixture models in order to find the most suitable number of models that works for their classification problem.

##Training Data Format You should use the ClassificationData data structure to train the GMM classifier.

##Example Code GMM Example