Supervised Novelty Detection

In this paper we present a novel approach and a new machine learning problem, called Supervised Novelty Detection (SND). This problem extends the One-Class Support Vector Machine setting for binary classification while keeping the nice properties of novelty detection problem at hand. To tackle this we approach binary classification from a new perspective using two different estimators and a coupled regularization term. It involves optimization over a different objective and a doubled set of Lagrange multipliers. One might consider our approach as a joint estimation of the support for different probability distributions per class where an ultimate goal is to separate classes with the largest possible angle between the normal vectors to the decision hyperplanes in the feature space. Regarding an obvious novelty of our problem we report and compare the results along the lines of standard C-SVM, LS-SVM and One-Class SVM. Experiments have demonstrated promising results that validate the usefulness of the proposed method.