Blind Data Classification using Hyper-Dimensional Convex Polytopes

Abstract

A blind classification algorithm is presented that uses hyperdimensional geometric algorithms to locate a hypothesis, in the form of a convex polytope or hyper-sphere. The convex polytope geometric model provides a well-fitted class representation that does not require training with instances of opposing classes. Further, the classification algorithm creates models for as many training classes of data as are available resulting in a hybrid anomaly/signature-based classifier. A method for handling non-numeric data types is explained. Classification accuracy is enhanced through the