Projections designs for compressive classification

This paper puts forth projections designs for compressive classification of Gaussian mixture models. In particular, we capitalize on the asymptotic characterization of the behavior of an (upper bound to the) misclassification probability associated with the optimal Maximum-A-Posteriori (MAP) classifier, which depends on quantities that are dual to the concepts of the diversity gain and coding gain in multi-antenna communications, to construct measurement designs that maximize the diversity-order of the measurement model. Numerical results demonstrate that the new measurement designs substantially outperform random measurements. Overall, the analysis and the designs cast geometrical insight about the mechanics of compressive classification problems.