Hierarchical K-means Clustering Using New Support Vector Machines for Multi-class Classification

Abstract

We propose a binary hierarchical classification structure to address the multi-class classification problem with a new hierarchical design method, k-means SVRM (support vector representation machine) clustering. This greatly improves upon our prior IJCNN hierarchical design. At each node in the hierarchy, we apply the SVRDM (support vector representation and discrimination machine) classifier, which offers generalization and good rejection ability. We also provide new theoretical bases and methods for our choice of the kernel function and new SVRDM parameter selection rules. Classification and rejection test results are presented on new databases of both simulated and real infra-red (IR) data.