Finding near neighbors through local search

Abstract

Proximity searching can be formulated as an optimization problem, being the goal function to find the object minimizing the distance to a given query by traversing a graph with a greedy algorithm. This formulation can be traced back to early formulations defined for vector spaces, and other recent approaches defined for the more general setup of metric spaces. In this paper we introduce three searching algorithms generalizing to local search other than greedy, and experimentally prove that our approach improves significantly the state of the art. In particular, our contributions have excellent trade-offs among speed, recall and memory usage; making our algorithms suitable for real world applications. As a byproduct, we present an open source implementation of most of the near neighbor search algorithms in the literature.