A Note on the Support Vector Data Description Problem

We consider the support vector data description problem which, given a set of data points, seeks to find a ball that minimizes an objective function incorporating both the radius of the ball and a penalty for any data point located outside the ball. We present a reduction of the problem to an unconstrained minimization of a strongly convex function, enabling it to be solved by a dual-based accelerated gradient method.