This paper proposes an optimal subsampling scheme in reproducing kernel Hilbert spaces, based on asymptotic analysis of an empirical risk minimizer with Horvitz-Thompson reweighting. The scheme, derived via the trace of the covariance operator, is shown to be implementable via plug-in and performs well on synthetic and real-world datasets.