Section: New Results

Near-Neighbor Preserving Dimension Reduction for Doubling Subsets of $L_1$

Participants : Ioannis Emiris, Ioannis Psarros.

In [21], we study randomized dimensionality reduction which 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 ($L_2$) metric, but much less for the Manhattan ($L_1$) metric. Our primary motivation is the approximate nearest neighbor problem in $L_1$. We exploit its reduction to the decision-with-witness version, called approximate 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 $L_2$ and $L_1$ metrics, as well as for doubling subsets of $L_2$. The case that remained open were doubling subsets of $L_1$. In this paper, we propose a dimension reduction by means of a near neighbor-preserving embedding for doubling subsets of $L_1$. Our approach is to represent the pointset with a carefully chosen covering set, then randomly project the latter. We study two types 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. We employ Cauchy variables: certain concentration bounds derived should be of independent interest.

This is joint work with Vassilis Margonis (NKUA), and is based on his MSc thesis.