Two rounds of linear-sum assignment identify near neighbors, a three-equation system estimates per-point noise, and subtracting the estimates from corrupted distances yields provably consistent Euclidean distances.
Laplacian eigenmaps for dimensionality reduction and data repre- sentation.Neural Computation, 15(6):1373–1396, 2003
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Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Two rounds of linear-sum assignment identify near neighbors, a three-equation system estimates per-point noise, and subtracting the estimates from corrupted distances yields provably consistent Euclidean distances.