English

The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction

Information Theory 2014-11-11 v1 Computational Geometry Data Structures and Algorithms Functional Analysis math.IT

Abstract

For any n>1n>1 and 0<ε<1/20<\varepsilon<1/2, we show the existence of an nO(1)n^{O(1)}-point subset XX of Rn\mathbb{R}^n such that any linear map from (X,2)(X,\ell_2) to 2m\ell_2^m with distortion at most 1+ε1+\varepsilon must have m=Ω(min{n,ε2logn})m = \Omega(\min\{n, \varepsilon^{-2}\log n\}). Our lower bound matches the upper bounds provided by the identity matrix and the Johnson-Lindenstrauss lemma, improving the previous lower bound of Alon by a log(1/ε)\log(1/\varepsilon) factor.

Keywords

Cite

@article{arxiv.1411.2404,
  title  = {The Johnson-Lindenstrauss lemma is optimal for linear dimensionality reduction},
  author = {Kasper Green Larsen and Jelani Nelson},
  journal= {arXiv preprint arXiv:1411.2404},
  year   = {2014}
}
R2 v1 2026-06-22T06:53:20.613Z