We survey permutation-based methods for approximate k-nearest neighbor search. In these
methods, every data point is represented by a ranked list of pivots sorted by the distance to …

We introduce a new probabilistic proximity search algorithm for range and A"-nearest
neighbor (A"-NN) searching in both coordinate and metric spaces. Although there exist …

Nearest neighbor search is a nearly ubiquitous problem in computer science. When nearest
neighbors are desired for a query set instead of a single query point, dual-tree algorithms …

We can define the nearest neighbor search (NSS) problem in the following way: given a set
of points P= p1, p2,..., pn in a metric space X, these points must be preprocessed in such a …

Our focus is on approximate nearest neighbor retrieval in metric and non-metric spaces. We
employ a VP-tree and explore two simple yet effective learning-to prune approaches: density …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

This paper describes BoostMap, a method for efficient nearest neighbor retrieval under
computationally expensive distance measures. Database and query objects are embedded …

We present PUFFINN, a parameterless LSH-based index for solving the $ k $-nearest
neighbor problem with probabilistic guarantees. By parameterless we mean that the user is …

This paper introduces ak-NN search index, the Rank Cover Tree (RCT), whose pruning tests
rely solely on the comparison of similarity values; other properties of the underlying space …

In this paper we present a novel technique for nearest neighbor searching dubbed
neighborhood approximation. The central idea is to divide the database into compact …