Path for Kernel Adaptive One-Class Support Vector Machine

Abstract

This paper proposes a Kernel Adaptive One Class SVM (KAOC-SVM) method based on the model introduced by A. Scholkopf and al. [7]. The aim is to find the solution path - the path of Lagrange multiplier a - as the kernel parameter changes from one value to another. It is similar to the regularization path approach proposed by Hastie and al. [2], which finds the path when the regularization parameter ? changes from 0 to 1. In present case, the main difference is that the Lagrange multiplier paths are not piecewise linear anymore. Experimental results show that the proposed method is able to compute one-class SVMs with the same accuracy as traditional method but exploring all solutions combining 2 kernels. Simulation results are presented and CPU requirement is analyzed.