Pattern Classification with Compact Distribution Maps

摘要

A difficult problem in classification is representing the class-conditional distributions concisely and faithfully. We propose a way of mapping such distributions and its use in constructing a similarity metric. A classifier using this metric can achieve low error rates and useful confidence scores permitting reliable reject behavior. We illustrate the method by an application in a challenging character recognition problem with thousands of classes. For applications to arbitrary domains, we present a method to automatically construct feature transformations that are suitable for such mappings.