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Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? by Verma, Nakul; Kpotufe, Samory; Dasgupta, Sanjoy is a scholarly article available to read on EtoBox.

What is Which Spatial Partition Trees are Adaptive to Intrinsic Dimension? about?

Recent theory work has found that a special type of spatial partition tree - called a random projection tree - is adaptive to the intrinsic dimension of the data from which it is built. Here we examine this same question, with a combination of theory and experiments, for a broader class of trees that includes k-d trees, dyadic trees, and PCA trees. Our motivation is to get a feel for (i) the kind of intrinsic low dimensional structure that can be empirically verified, (ii) the extent to which a spatial partition can exploit such structure, and (iii) the implications for standard statistical tasks such as regression, vector quantization, and nearest neighbor search.

Author

Verma, Nakul; Kpotufe, Samory; Dasgupta, Sanjoy

Published

2012

Language

EN