Near neighbor preserving dimension reduction for doubling subsets of $\ell_1$.

Randomized dimensionality reduction has been recognized as one of the fundamental techniques in handling high-dimensional data. Starting with the celebrated Johnson-Lindenstrauss Lemma, such reductions have been studied in depth for the Euclidean $(ell_2)$ metric and, much less, for the Manhattan $(ell_1)$ metric. Our primary motivation is the approximate nearest neighbor problem in $ell_1$. We exploit its reduction to the decision-with-witness version, called approximate textit{near} neighbor, which incurs a roughly logarithmic overhead. In 2007, Indyk and Naor, in the context of approximate nearest neighbors, introduced the notion of nearest neighbor-preserving embeddings. These are randomized embeddings between two metric spaces with guaranteed bounded distortion only for the distances between a query point and a point set. Such embeddings are known to exist for both $ell_2$ and $ell_1$ metrics, as well as for doubling subsets of $ell_2$. In this paper, we propose a dimension reduction, textit{near} neighbor-preserving embedding for doubling subsets of $ell_1$. Our approach is to represent the point set with a carefully chosen covering set, and then apply a random projection to that covering set. We study two cases of covering sets: $c$-approximate $r$-nets and randomly shifted grids, and we discuss the tradeoff between them in terms of preprocessing time and target dimension.