A kernel-based two-stage one-class support vector machines algorithm

Abstract

One-class SVM is a kernel-based method that utilizes the kernel trick for data clustering. However it is only able to detect one cluster of non-convex shape in the input space. In this study, we propose an iterative two-stage one-class SVM to cluster data into several groups. In the first stage, one-class SVM is used to find an optimal weight vector for each cluster in the feature space, while in the second stage the weight vector is used to refine the clustering result. A mechanism is provided to control the optimal hyperplane to work against outliers. Experimental results have shown that our method compares favorably with other kernel based clustering algorithms, such as KKM and KFCM on several synthetic data sets and UCI real data sets. © Springer-Verlag Berlin Heidelberg 2007.