Randomly-oriented k-d Trees Adapt to Intrinsic Dimension

Author Santosh S. Vempala

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Santosh S. Vempala

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Santosh S. Vempala. Randomly-oriented k-d Trees Adapt to Intrinsic Dimension. In IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2012). Leibniz International Proceedings in Informatics (LIPIcs), Volume 18, pp. 48-57, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


The classic k-d tree data structure continues to be widely used in spite of its vulnerability to the so-called curse of dimensionality. Here we provide a rigorous explanation: for randomly rotated data, a k-d tree adapts to the intrinsic dimension of the data and is not affected by the ambient dimension, thus keeping the data structure efficient for objects such as low-dimensional manifolds and sparse data. The main insight of the analysis can be used as an algorithmic pre-processing step to realize the same benefit: rotate the data randomly; then build a k-d tree. Our work can be seen as a refinement of Random Projection trees [Dasgupta 2008], which also adapt to intrinsic dimension but incur higher traversal costs as the resulting cells are polyhedra and not cuboids. Using k-d trees after a random rotation results in cells that are cuboids, thus preserving the traversal efficiency of standard k-d trees.
  • Data structures
  • Nearest Neighbors
  • Intrinsic Dimension
  • k-d Tree


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