Learning the Kernel in Mahalanobis One-Class Support Vector Machines

Abstract

In this paper, we show that one-class SVMs can also utilize data covariance in a robust manner to improve performance. Furthermore, by constraining the desired kernel function as a convex combination of base kernels, we show that the weighting coefficients can be learned via quadratically constrained quadratic programming (QCQP) or second order cone programming (SOCP) methods. Performance on both toy and real-world data sets show promising results. This paper thus offers another demonstration of the synergy between convex optimization and kernel methods.