Ramp loss support vector data description

Abstract

Data description is an important problem that has many applications. Despite the great success, the popular support vector data description (SVDD) has problem with generalization and scalability when training data contains a significant amount of outliers. We propose in this paper the so-called ramp loss SVDD then prove its scalability and robustness. For solving the proposed problem, we develop an efficient algorithm based on DC (Difference of Convex functions) programming and DCA (DC Algorithm). Preliminary experiments on both synthetic and real data show the efficiency of our approach.