English

Improving the Johnson-Lindenstrauss Lemma

Machine Learning 2010-05-11 v1

Abstract

The Johnson-Lindenstrauss Lemma allows for the projection of nn points in pp-dimensional Euclidean space onto a kk-dimensional Euclidean space, with k24lnn3ϵ22ϵ3k \ge \frac{24\ln \emph{n}}{3\epsilon^2-2\epsilon^3}, so that the pairwise distances are preserved within a factor of 1±ϵ1\pm\epsilon. Here, working directly with the distributions of the random distances rather than resorting to the moment generating function technique, an improvement on the lower bound for kk is obtained. The additional reduction in dimension when compared to bounds found in the literature, is at least 13%13\%, and, in some cases, up to 30%30\% additional reduction is achieved. Using the moment generating function technique, we further provide a lower bound for kk using pairwise L2L_2 distances in the space of points to be projected and pairwise L1L_1 distances in the space of the projected points. Comparison with the results obtained in the literature shows that the bound presented here provides an additional 3640%36-40\% reduction.

Keywords

Cite

@article{arxiv.1005.1440,
  title  = {Improving the Johnson-Lindenstrauss Lemma},
  author = {Javier Rojo and Tuan Nguyen},
  journal= {arXiv preprint arXiv:1005.1440},
  year   = {2010}
}

Comments

24 pages, 2 tables

R2 v1 2026-06-21T15:20:21.952Z