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Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery (2005)

Jeremy Kubica, Joseph Masiero, Andrew Moore, Robert Jedicke, Andrew Connolly

Abstract

In this paper we consider the problem of finding sets of points that conform to a given underlying model from within a dense, noisy set of observations. This problem is motivated by the task of efficiently linking faint asteroid detections, but is applicable to a range of spatial queries. We survey current tree-based approaches, showing a trade-off exists between single tree and multiple tree algorithms. To this end, we present a new type of multiple tree algorithm that uses a variable number of trees to exploit the advantages of both approaches. We empirically show that this algorithm performs well using both simulated and astronomical data.

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Approximate BibTeX Entry

@inproceedings{kubicaNIPS05,
    Month = {December},
    Year = {2005},
    Booktitle = {Advances in Neural Information Processing Systems},
    Title = {Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery}
}