Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques

Volume 5171 of the series Lecture Notes in Computer Science pp 523-534

Near Optimal Dimensionality Reductions That Preserve Volumes

  • Avner MagenAffiliated withDepartment of Computer Science, University of Toronto
  • , Anastasios ZouziasAffiliated withDepartment of Computer Science, University of Toronto


Let P be a set of n points in Euclidean space and let 0 < ε< 1. A well-known result of Johnson and Lindenstrauss states that there is a projection of P onto a subspace of dimension \(\mathcal{O}(\epsilon^{-2} \log n)\) such that distances change by at most a factor of 1 + ε. We consider an extension of this result. Our goal is to find an analogous dimension reduction where not only pairs but all subsets of at most k points maintain their volume approximately. More precisely, we require that sets of size s ≤ k preserve their volumes within a factor of (1 + ε)s − 1. We show that this can be achieved using \(\mathcal{O}(\max\{\frac{k}{\epsilon},\epsilon^{-2}\log n\})\) dimensions. This in particular means that for \(k = \mathcal{O}(\log n/\epsilon)\) we require no more dimensions (asymptotically) than the special case k = 2, handled by Johnson and Lindenstrauss. Our work improves on a result of Magen (that required as many as \(\mathcal{O}(k\epsilon^{-2}\log n)\) dimensions) and is tight up to a factor of \(\mathcal{O}(1/\epsilon)\). Another outcome of our work is an alternative and greatly simplified proof of the result of Magen showing that all distances between points and affine subspaces spanned by a small number of points are approximately preserved when projecting onto \(\mathcal{O}(k\epsilon^{-2}\log n)\) dimensions.