DeepAI AI Chat
Log In Sign Up

Adaptive Estimation for Approximate k-Nearest-Neighbor Computations

by   Daniel LeJeune, et al.

Algorithms often carry out equally many computations for "easy" and "hard" problem instances. In particular, algorithms for finding nearest neighbors typically have the same running time regardless of the particular problem instance. In this paper, we consider the approximate k-nearest-neighbor problem, which is the problem of finding a subset of O(k) points in a given set of points that contains the set of k nearest neighbors of a given query point. We propose an algorithm based on adaptively estimating the distances, and show that it is essentially optimal out of algorithms that are only allowed to adaptively estimate distances. We then demonstrate both theoretically and experimentally that the algorithm can achieve significant speedups relative to the naive method.


page 1

page 2

page 3

page 4


Leveraging Reinforcement Learning for evaluating Robustness of KNN Search Algorithms

The problem of finding K-nearest neighbors in the given dataset for a gi...

Finding Relevant Points for Nearest-Neighbor Classification

In nearest-neighbor classification problems, a set of d-dimensional trai...

Coresets for the Nearest-Neighbor Rule

The problem of nearest-neighbor condensation deals with finding a subset...

Scalable Secure Computation of Statistical Functions with Applications to k-Nearest Neighbors

Given a set S of n d-dimensional points, the k-nearest neighbors (KNN) i...

Point Localization and Density Estimation from Ordinal kNN graphs using Synchronization

We consider the problem of embedding unweighted, directed k-nearest neig...

Certifiable Robustness for Nearest Neighbor Classifiers

ML models are typically trained using large datasets of high quality. Ho...