An efficient algorithm extracts clusters to denoise distances to fixed accuracy in metric measure spaces under lower phi-regularity, with a non-efficient method for higher accuracy indicating a statistical-computational gap unlike the Riemannian case.
and Priebe, Carey E
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Denoising Distances in Metric Measure Spaces
An efficient algorithm extracts clusters to denoise distances to fixed accuracy in metric measure spaces under lower phi-regularity, with a non-efficient method for higher accuracy indicating a statistical-computational gap unlike the Riemannian case.