One-Class SVM

Many approaches are based on the estimation of the density of probability for the normal data. Anomalies correspond to those samples where the density of probability is “very low”.

Now, SVMs are max-margin methods, i.e. they do not model a probability distribution. Here the idea is to find a function that is positive for regions with high density of points, and negative for small densities.

One-Class SVM is similar, but instead of using a hyperplane to separate two classes of instances, it uses a hypersphere to encompass all of the instances. Now think of the “margin” as referring to the outside of the hypersphere – so by “the largest possible margin”, we mean “the smallest possible hypersphere”.