Nearest-neighbor searching under uncertainty II

Pankaj K. Agarwal, Boris Aronov, Sariel Har-Peled, Jeff M. Phillips, Ke Yi, Wuzhou Zhang

Research output: Contribution to journalArticlepeer-review

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 a 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 (i) computing all points that are nearest neighbors of a query point with nonzero probability and (ii) estimating the probability of a point being the nearest neighbor of a query point, either exactly or within a specified additive error.

Original languageEnglish (US)
Article number3
JournalACM Transactions on Algorithms
Volume13
Issue number1
DOIs
StatePublished - Oct 2016

Keywords

  • Approximate nearest neighbor
  • Indexing uncertain data
  • Probabilistic nearest neighbor
  • Threshold queries

ASJC Scopus subject areas

  • Mathematics (miscellaneous)

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