A minimax approach to development of robust discrimination algorithms for multivariate mixture distributions

Addresses the two class discrimination problem in the case that each class can be viewed as being composed of a finite number of types. The prior probabilities for the types comprising both classes are unknown, the class costs are known and the conditional densities for the types are real analytic functions. An algorithm is presented that can be used to estimate an optimal discriminant function that is robust in the minimax sense. The algorithm involves a search over a set of prior weights. The convergence properties of the algorithm are examined in a series of tests involving Gaussian mixture densities