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 journalArticle

    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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  • Cite this

    Agarwal, P. K., Aronov, B., Har-Peled, S., Phillips, J. M., Yi, K., & Zhang, W. (2016). Nearest-neighbor searching under uncertainty II. ACM Transactions on Algorithms, 13(1), [3]. https://doi.org/10.1145/2955098