Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)
Colin Campbell, Kristin Bennett
Novelty detection involves modeling the normal behaviour of a sys(cid:173) tem hence enabling detection of any divergence from normality. It has potential applications in many areas such as detection of ma(cid:173) chine damage or highlighting abnormal features in medical data. One approach is to build a hypothesis estimating the support of the normal data i.e. constructing a function which is positive in the region where the data is located and negative elsewhere. Recently kernel methods have been proposed for estimating the support of a distribution and they have performed well in practice - training involves solution of a quadratic programming problem. In this pa(cid:173) per we propose a simpler kernel method for estimating the support based on linear programming. The method is easy to implement and can learn large datasets rapidly. We demonstrate the method on medical and fault detection datasets.