An algorithm extracts large localized clusters in metric measure spaces to denoise distances with near-linear time for fixed error r, plus sharp info-theoretic scales for vanishing r suggesting statistical-computational gaps beyond Riemannian cases.
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Denoising Distances in Metric Measure Spaces
An algorithm extracts large localized clusters in metric measure spaces to denoise distances with near-linear time for fixed error r, plus sharp info-theoretic scales for vanishing r suggesting statistical-computational gaps beyond Riemannian cases.