Power sum kernels in permutation learning

In this paper, we consider the use of power sum kernels in solving the problem of permutation learning. We present a way to approximate a symmetrized kernel that naturally arises in this problem using the Monte Carlo method and estimate the convergence rate. We also touch on the problem of partial rankings and present some results for the case when the number of fixed elements is 1 or 2.