Support Vector Representation of Multi-categorical Data

Abstract

We propose a new algorithm for the categorisation of data into multiple classes. It minimises a quadratic homogeneous program, and can be viewed as a generalisation of the well known support vector machines to multiple classes. For only one class it reduces to a quadratic problem, whose solution can be seen as an estimate of the support of a distribution. Given a set of labelled data, our algorithm estimates for each class a representative vector in a feature space. Each of these vectors is expressible as a linear combination of the training data in its class, mapped into feature space. Therefore our algorithm needs less parameters than other multi-class support vector approaches.