Optimal (Euclidean) Metric Compression
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Abstract (arXiv)
We study the problem of representing all distances between n points in mathbb Rd, with arbitrarily small distortion, using as few bits as possible. We give asymptotically tight bounds for this problem, for Euclidean metrics, for ℓ1 (a.k.a.~Manhattan) metrics, and for general metrics. Our bounds for Euclidean metrics mark the first improvement over compression schemes based on discretizing the classical dimensionality reduction theorem of Johnson and Lindenstrauss (Contemp.~Math.~1984). Since it is known that no better dimension reduction is possible, our results establish that Euclidean metric compression is possible beyond dimension reduction.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2021.
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