A new online learning with Kernels method in novelty detection

A new optimization problem together with its model for online learning with kernels in novelty detection is formulated in a Reproducing Kernel Hilbert Space (RKHS). By exploiting the techniques of Lagrange dual problem in a similar way to Vapnik´s support vector machine (SVM), the optimization problem is solved iteratively and this gives an algorithm named online learning with kernels denoted as (OLK_N). The algorithm is applied to novelty detection including real time background substraction. Such successful applications illustrate the effectiveness of the OLK_N in novelty detection.