URerF uses unsupervised decision forests on sparse linear feature combinations to estimate geodesic distances robustly under high-dimensional noise, outperforming Isomap, UMAP, and FLANN on simulated and connectome data.
Classification and regression by randomforest,
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Geodesic Learning via Unsupervised Decision Forests
URerF uses unsupervised decision forests on sparse linear feature combinations to estimate geodesic distances robustly under high-dimensional noise, outperforming Isomap, UMAP, and FLANN on simulated and connectome data.