The p-Center machine

We present a new approach to find an optimal large margin classifier based on the p-center which was proposed by Moretti in 2003. Starting with the p-Center of a general polytope, we extend this definition to a polyhedral cone, and introduce an algorithm approximating the p-Center of the version space, which we call p-Center machine (PCM). In addition, we present a large-scale and a soft boundary version of the PCM, and compare their performance to the support vector machine and the Bayes point machine. It turns out that the p-Center is close to the Bayes point and is similar in performance to the support vector machine as well as the Bayes point machine. Additionally, the proposed algorithm is highly parallelizable and thus very efficient in terms of computational effort.