Nearest-Neighbor Searching Under Uncertainty II

TL;DR

This paper develops efficient algorithms for probabilistic nearest-neighbor searches where data points have uncertain locations described by probability distributions, addressing real-world applications with imprecise data.

Contribution

It introduces algorithms for identifying all potential nearest neighbors with nonzero probability and estimating their likelihoods within a probabilistic framework.

Findings

Algorithms efficiently identify all probable nearest neighbors.

Methods accurately estimate nearest neighbor probabilities.

Applicable to sensor data, location services, and face recognition.

Abstract

Nearest-neighbor search, which returns the nearest neighbor of a query point in a set of points, is an important and widely studied problem in many fields, and it has wide range of applications. In many of them, such as sensor databases, location-based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest-neighbor queries in a probabilistic framework in which the location of each input point is specified as a probability distribution function. We present efficient algorithms for - computing all points that are nearest neighbors of a query point with nonzero probability; and - estimating the probability of a point being the nearest neighbor of a query point, either exactly or within a specified additive error.