# Near Neighbor Search via Efficient Average Distortion Embeddings

arXiv: https://arxiv.org/abs/2105.04712 · HTML: https://mlsanity.com/paper/2105.04712/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2021.

## Built on by (most cited first, 1 of 1)

- [Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures](https://mlsanity.com/paper/2107.06626/) (2021)

## Abstract (arXiv)

A recent series of papers by Andoni, Naor, Nikolov, Razenshteyn, and Waingarten (STOC 2018, FOCS 2018) has given approximate near neighbour search (NNS) data structures for a wide class of distance metrics, including all norms. In particular, these data structures achieve approximation on the order of p for ℓpd norms with space complexity nearly linear in the dataset size n and polynomial in the dimension d, and query time sub-linear in n and polynomial in d. The main shortcoming is the exponential in d pre-processing time required for their construction. In this paper, we describe a more direct framework for constructing NNS data structures for general norms. More specifically, we show via an algorithmic reduction that an efficient NNS data structure for a given metric is implied by an efficient average distortion embedding of it into ℓ1 or into Euclidean space. In particular, the resulting data structures require only polynomial pre-processing time, as long as the embedding can be computed in polynomial time. As a concrete instantiation of this framework, we give an NNS data structure for ℓp with efficient pre-processing that matches the approximation factor, space and query complexity of the aforementioned data structure of Andoni et al. On the way, we resolve a question of Naor (Analysis and Geometry in Metric Spaces, 2014) and provide an explicit, efficiently computable embedding of ℓp, for p ≥ 2, into ℓ2 with (quadratic) average distortion on the order of p. We expect our approach to pave the way for constructing efficient NNS data structures for all norms.