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.
arXiv preprint arXiv:2203.15351 , year =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.