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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.

Notes

Associated Lab(s) / Group(s): Auton Lab

Associated Project(s): Auton Project

Text Reference

Jeremy Martin Kubica, Joseph Masiero, Andrew Moore, Robert Jedicke, and Andrew J. Connolly, "Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery," Neural Information Processing Systems, December, 2005.

BibTeX Reference

@inproceedings{Kubica_2005_5283,
   author = "Jeremy Martin Kubica and Joseph Masiero and Andrew Moore and Robert Jedicke and Andrew J Connolly",
   title = "Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery",
   booktitle = "Neural Information Processing Systems",
   month = "December",
   year = "2005",
}

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