Multiclass Classification Using Gaussian Mixture Models With Scikit Learn

I am trying to use sklearn.mixture.GaussianMixture for classification of pixels in an hyper-spectral image. There are 15 classes (1-15). I tried using the method http://scikit-lear

Solution 1:

Gaussian Mixture is not a classifier. It is a density estimation method, and expecting that its components will magically align with your classes is not a good idea. You should try out actual supervised techniques, since you clearly do have access to labels. Scikit-learn offers lots of these, including Random Forest, KNN, SVM, ... pick your favourite. GMM simply tries to fit mixture of Gaussians into your data, but there is nothing forcing it to place them according to the labeling (which is not even provided in the fit call). From time to time this will work - but only for trivial problems, where classes are so well separated that even Naive Bayes would work, in general however it is simply invalid tool for the problem.

Solution 2:

GMM is not a classifier, but generative model. You can use it to a classification problem by applying Bayes theorem. It's not true that classification based on GMM works only for trivial problems. However it's based on mixture of Gauss components, so fits the best problems with high level features.

Your code incorrectly use GMM as classifier. You should use GMM as a posterior distribution, one GMM per each class.