A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers

We present a small sphere and large margin approach for novelty detection problems, where the majority of training data are normal examples. In addition, the training data also contain a small number of abnormal examples or outliers. The basic idea is to construct a hypersphere that contains most of the normal examples, such that the volume of this sphere is as small as possible, while at the same time the margin between the surface of this sphere and the outlier training data is as large as possible. This can result in a closed and tight boundary around the normal data. To build such a sphere, we only need to solve a convex optimization problem that can be efficiently solved with the existing software packages for training nu-support vector machines. Experimental results are provided to validate the effectiveness of the proposed algorithm.