New weighted support vector K-means clustering for hierarchical multi-class classification

We propose a binary hierarchical classification structure to address the multi-class classification problem with a new hierarchical design method, weighted support vector k-means clustering, which automatically separates a set of classes into two smaller groups at each node in the hierarchy. This method is able to visualize and cluster high-dimensional support vector data; therefore, it greatly improves upon prior hierarchical classifler design. At each node in the hierarchy, we apply an SVRDM (support vector representation and discrimination machine) classifler, which offers generalization and good rejection of unseen false objects, which is not achieved by the standard SVM classifier. We provide a new theoretical basis for the good SVRDM rejection obtained, due to its looser constrained optimization problem, compared to that of an SVM. New classification and rejection test results are presented on a real IR (infra-red) database.