Terminal Embeddings
Not on the map. Why?
Abstract (arXiv)
In this paper we study em terminal embeddings, in which one is given a finite metric (X,dX) (or a graph G=(V,E)) and a subset K subseteq X of its points are designated as em terminals. The objective is to embed the metric into a normed space, while approximately preserving all distances among pairs that contain a terminal. We devise such embeddings in various settings, and conclude that even though we have to preserve ≈|K|· |X| pairs, the distortion depends only on |K|, rather than on |X|. We also strengthen this notion, and consider embeddings that approximately preserve the distances between em all pairs, but provide improved distortion for pairs containing a terminal. Surprisingly, we show that such embeddings exist in many settings, and have optimal distortion bounds both with respect to X × X and with respect to K × X. Moreover, our embeddings have implications to the areas of Approximation and Online Algorithms. In particular, [ALN08] devised an tildeO(√log r)-approximation algorithm for sparsest-cut instances with r demands. Building on their framework, we provide an tildeO(√log |K|)-approximation for sparsest-cut instances in which each demand is incident on one of the vertices of K (aka, terminals). Since |K| ≤ r, our bound generalizes that of [ALN08].
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2016.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 0; 2025: 1.
5 papers.
- 2022Towards Optimal Lower Bounds for k-median and k-means Coresetscited by 23
- 2021Near-optimal Algorithms for Explainable k-Medians and k-Meanscited by 13
- 2022An Empirical Evaluation of k-Means Coresetscited by 7
- 2021Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measurescited by 0
- 2025Beyond Worst-Case Dimensionality Reduction for Sparse Vectorscited by 0
- 2025Beyond Worst-Case Dimensionality Reduction for Sparse Vectors27 Feb 2025
- 2022An Empirical Evaluation of k-Means Coresets3 Jul 2022
- 2022Towards Optimal Lower Bounds for k-median and k-means Coresets25 Feb 2022
- 2021Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures14 Jul 2021
- 2021Near-optimal Algorithms for Explainable k-Medians and k-Means2 Jul 2021