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Overview:

Random Projection Trees is a recursive space partitioning datastructure which can automatically adapt to the underlying (linear or non-linear) structure in data. It has strong theoretical guarantees on rates of convergence and works well in practice.

You can use RPTrees to learn the structure of manifolds, perform fast nearest-neighbor searches, do vector-quantization of the underlying density, and much more.

To learn more, go the the tutorials page, or see the full publication.