Low-dimensional Projections in Gaussian Mixture Model Classification Riduzione di dimensioni nell’analisi discriminante basata su modelli mistura di gaussiane

In this paper we present a strategy for producing low-dimensional projections that maximally separate the classes in Gaussian Mixture Model classification. The most revealing subspaces are those along which the classes are maximally separable. Here we consider a particular probability product kernel as a measure of similarity or affinity between the class conditional distributions. It takes an appealing closed analytical form in the case of Gaussian mixture components. The performance of the proposed strategy has been evaluated on simulated and real data.