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.
On the curse of dimensionality for Normalizing Flows
Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning
Bias-corrected estimator for intrinsic dimension and differential entropy--a visual multiscale approach
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