Escaping the curse of dimensionality with a tree-based regressor

Samory Kpotufe

Escaping the curse of dimensionality with a tree-based regressor

Samory Kpotufe

Operations Research & Financial Engineering

Research output: Contribution to conference › Paper › peer-review

13 Scopus citations

Abstract

We present the first tree-based regressor whose convergence rate depends only on the intrinsic dimension of the data, namely its Assouad dimension. The regressor uses the RPtree partitioning procedure, a simple randomized variant of k-d trees.

Original language	English (US)
State	Published - 2009
Event	22nd Conference on Learning Theory, COLT 2009 - Montreal, QC, Canada Duration: Jun 18 2009 → Jun 21 2009

Other

Other	22nd Conference on Learning Theory, COLT 2009
Country/Territory	Canada
City	Montreal, QC
Period	6/18/09 → 6/21/09

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Education

Cite this

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title = "Escaping the curse of dimensionality with a tree-based regressor",

abstract = "We present the first tree-based regressor whose convergence rate depends only on the intrinsic dimension of the data, namely its Assouad dimension. The regressor uses the RPtree partitioning procedure, a simple randomized variant of k-d trees.",

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M3 - Paper

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T2 - 22nd Conference on Learning Theory, COLT 2009

Y2 - 18 June 2009 through 21 June 2009

ER -

Escaping the curse of dimensionality with a tree-based regressor

Abstract

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