Truncated optimal entropy-minimizing expansions can serve to characterize classes of multivariate data. A method is presented here by which the level of truncation and the corresponding dimensionalities of the class subspaces can be chosen to ensure adequate discrimination. The subspaces are chosen to maximize the average margin of correct classification of the paradigms of one class subject to constraints on the other margins.