Cover trees for nearest neighbor

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Cover trees for nearest neighbor (2006)

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by Alina Beygelzimer , Sham Kakade , John Langford

Venue:	In Proceedings of the 23rd international conference on Machine learning
Citations:	210 - 0 self

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@INPROCEEDINGS{Beygelzimer06covertrees,
    author = {Alina Beygelzimer and Sham Kakade and John Langford},
    title = {Cover trees for nearest neighbor},
    booktitle = {In Proceedings of the 23rd international conference on Machine learning},
    year = {2006},
    pages = {97--104},
    publisher = {ACM}
}

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Abstract

ABSTRACT. We present a tree data structure for fast nearest neighbor operations in general-point metric spaces. The data structure requires space regardless of the metric’s structure. If the point set has an expansion constant � in the sense of Karger and Ruhl [KR02], the data structure can be constructed in � time. Nearest neighbor queries obeying the expansion bound require � time. In addition, the nearest neighbor of points can be queried in time. We experimentally test the algorithm showing speedups over the brute force search varying between 1 and 2000 on natural machine learning datasets. 1.

Keyphrases

cover tree data structure space regardless metric structure natural machine ruhl kr02 point set require time neighbor operation tree data structure neighbor query expansion constant brute force search general-point metric space

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