An affine invariant $k$-nearest neighbor regression estimate

Abstract

We design a data-dependent metric in ${\mathbb{R}}^d$ and use it to define the $k$-nearest neighbors of a given point. Our metric is invariant under all affine transformations. We show that, with this metric, the standard $k$-nearest neighbor regression estimate is asymptotically consistent under the usual conditions on $k$, and minimal requirements on the input data.