Generative models for similarity-based classification

Abstract

A maximum-entropy approach to generative similarity-based classifiers model is proposed. First, a descriptive set of similarity statistics is assumed to be sufficient for classification. Then the class-conditional distributions of these descriptive statistics are estimated as the maximum-entropy distributions subject to empirical moment constraints. The resulting exponential class-conditional distributions are used in a maximum a posteriori decision rule, forming the similarity discriminant analysis (SDA) classifier. Simulated and real data experiments compare performance to the k-nearest neighbor classifier, the nearest-centroid classifier, and the potential support vector machine (PSVM).