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RI | Publications | Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery
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Text only version of this site
Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery
J.M. Kubica, J. Masiero, A. Moore, R. Jedicke, and A.J. Connolly
Neural Information Processing Systems, December, 2005.
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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/group: Auton Lab
Associated project: Auton Project
| Text Reference |
J.M. Kubica, J. Masiero, A. Moore, R. Jedicke, and A.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"
}