Astronomy and Astrophysics – Astrophysics
Scientific paper
2004-01-08
Astronomy and Astrophysics
Astrophysics
4-page conference proceeding based on talk given at ADASS XIII, 13-15 October, 2003, Strasbourg
Scientific paper
Many fundamental statistical methods have become critical tools for scientific data analysis yet do not scale tractably to modern large datasets. This paper will describe very recent algorithms based on computational geometry which have dramatically reduced the computational complexity of 1) kernel density estimation (which also extends to nonparametric regression, classification, and clustering), and 2) the n-point correlation function for arbitrary n. These new multi-tree methods typically yield orders of magnitude in speedup over the previous state of the art for similar accuracy, making millions of data points tractable on desktop workstations for the first time.
Connolly Andrew J.
Genovese Christopher
Gray Alexander G.
Moore Andrew W.
Nichol Robert C.
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