Abstract
Let X be a subset of n points of the Euclidean space, and let 0 < ɛ < 1. A classical result of Johnson and Lindenstrauss [JL84] states that there is a projection of X onto a subspace of dimension O(ɛ-2 log n), with distortion ≤ 1 + ɛ. Here we show a natural extension of the above result, to a stronger preservation of the geometry of finite spaces. By a k-fold increase of the number of dimensions used compared to [JL84], a good preservation of volumes and of distances between points and affine spaces is achieved. Specifically, we show it is possible to embed a subset of size n of the Euclidean space into a O(ɛ-2 klogn)-dimensional Euclidean space, so that no set of size s ≤ k changes its volume by more than (1+ɛ)s-1. Moreover, distances of points from affine hulls of sets of at most k-1 points in the space do not change by more than a factor of 1 + ɛ. A consequence of the above with k = 3 is that angles can be preserved using asymptotically the same number of dimensions as the one used in [JL84]. Our method can be applied to many problems with high-dimensional nature such as Projective Clustering and Approximated Nearest Affine Neighbor Search. In particular, it shows a first poly-logarithmic query time approximation algorithm to the latter. We also show a structural application that for volume respecting embedding in the sense introduced by Feige [Fei00], the host space need not generally be of dimensionality greater than polylogarithmic in the size of the graph.
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Magen, A. (2002). Dimensionality Reductions That Preserve Volumes and Distance to Affine Spaces, and Their Algorithmic Applications. In: Rolim, J.D.P., Vadhan, S. (eds) Randomization and Approximation Techniques in Computer Science. RANDOM 2002. Lecture Notes in Computer Science, vol 2483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45726-7_19
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DOI: https://doi.org/10.1007/3-540-45726-7_19
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