Given two classes of training data (A and B), I want to fit each class' distribution using a GMM with k components, and then use Bayes Decision Model for the classification.
The first step was to use PCA on data set A, project the data into a lower-dimensional subspace, and then for each class do the actually fitting.
In MATLAB this would be implemented as:
>> Aobj = gmdistribution.fit(A, k, 'Regularize', 1e-5); >> Bobj = gmdistribution.fit(B, k, 'Regularize', 1e-5);
However, when I evaluate the pdf for the the training data:
>> pdf(Aobj, A)
one or two of the data points are assigned huge values (3.200989873206918e+241).
What am I missing here?
Data set: collection of 50x25 grey-scale images with A and B indicating the presence or absence of pedestrians.