Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions
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Abstract (arXiv)
We introduce and study the notion of an outer bi-Lipschitz extension of a map between Euclidean spaces. The notion is a natural analogue of the notion of a Lipschitz extension of a Lipschitz map. We show that for every map f there exists an outer bi-Lipschitz extension f' whose distortion is greater than that of f by at most a constant factor. This result can be seen as a counterpart of the classic Kirszbraun theorem for outer bi-Lipschitz extensions. We also study outer bi-Lipschitz extensions of near-isometric maps and show upper and lower bounds for them. Then, we present applications of our results to prioritized and terminal dimension reduction problems. * We prove a prioritized variant of the Johnson-Lindenstrauss lemma: given a set of points Xsubset ℝd of size N and a permutation ("priority ranking") of X, there exists an embedding f of X into ℝO(log N) with distortion O(log log N) such that the point of rank j has only O(log3 + varepsilon j) non-zero coordinates - more specifically, all but the first O(log3+varepsilon j) coordinates are equal to 0; the distortion of f restricted to the first j points (according to the ranking) is at most O(loglog j). The result makes a progress towards answering an open question by Elkin, Filtser, and Neiman about prioritized dimension reductions. * We prove that given a set X of N points in ℝd, there exists a terminal dimension reduction embedding of ℝd into ℝd', where d' = Oleft(fraclog Nvarepsilon4right), which preserves distances |x-y| between points x∈ X and y ∈ ℝd, up to a multiplicative factor of 1 ± varepsilon. This improves a recent result by Elkin, Filtser, and Neiman. The dimension reductions that we obtain are nonlinear, and this nonlinearity is necessary.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Nov 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 1; 2025: 1.
7 papers.
- 2022Towards Optimal Lower Bounds for k-median and k-means Coresetscited by 23
- 2022An Empirical Evaluation of k-Means Coresetscited by 7
- 2021Terminal Embeddings in Sublinear Timecited by 6
- 2023A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reductioncited by 4
- 2022On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Low-Dimensional Submanifolds of ℝNcited by 1
- 2024Distance-Preserving Representations for Genomic Spatial Reconstructioncited by 0
- 2025Beyond Worst-Case Dimensionality Reduction for Sparse Vectorscited by 0
- 2025Beyond Worst-Case Dimensionality Reduction for Sparse Vectors27 Feb 2025
- 2024Distance-Preserving Representations for Genomic Spatial Reconstruction1 Aug 2024
- 2023A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction11 Jun 2023
- 2022An Empirical Evaluation of k-Means Coresets3 Jul 2022
- 2022On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Low-Dimensional Submanifolds of ℝN7 Jun 2022
- 2022Towards Optimal Lower Bounds for k-median and k-means Coresets25 Feb 2022
- 2021Terminal Embeddings in Sublinear Time17 Oct 2021