Minimax Estimation of Kernel Mean Embeddings

In this paper, we study the minimax estimation of the Bochner integral \[ \mu_k(P) := \int_\mathcal{X} k(\cdot,x)\, dP(x), \] also called the kernel mean embedding, based on random samples drawn i.i.d. from $P$, where $k:\mathcal{X}\times\mathcal{X}\rightarrow \mathbb{R}$ is a positive definite kernel. Various estimators (including the empirical estimator), $\hat{\theta}_n$ of $\mu_k(P)$ are studied in the literature wherein all of them satisfy $\|\hat{\theta}_n-\mu_k(P)\|_{\mathcal{H}_k}=O_P(n^{-1/2})$ with $\mathcal{H}_k$ being the reproducing kernel Hilbert space induced by $k$. The main contribution of the paper is in showing that the above mentioned rate of $n^{-1/2}$ is minimax in $\|\cdot\|_{\mathcal{H}_k}$ and $\|\cdot\|_{L^2(\mathbb{R}^d)}$-norms over the class of discrete measures and the class of measures that has an infinitely differentiable density, with $k$ being a continuous translation- invariant kernel on $\mathbb{R}^d$. The interesting aspect of this result is that the minimax rate is independent of the smoothness of the kernel and the density of $P$ (if it exists). [abs] [ pdf ][ bib ] © JMLR 2017. (edit, beta)