A Quantized Johnson Lindenstrauss Lemma: The Finding of Buffon's Needle
Laurent Jacques

TL;DR
This paper introduces a quantized version of the Johnson-Lindenstrauss lemma inspired by Buffon's needle problem, enabling approximate distance preservation in high-dimensional data with quantization effects analyzed.
Contribution
It derives a quantized JL lemma using geometric probability, showing how linear dimensionality reduction combined with quantization affects distance preservation.
Findings
The quantized JL mapping approximately preserves distances with additive and multiplicative distortions.
Distortions decay as the inverse square root of the embedding dimension M.
For coarse quantization, distortions are mainly additive; for fine quantization, embeddings approach isometry.
Abstract
In 1733, Georges-Louis Leclerc, Comte de Buffon in France, set the ground of geometric probability theory by defining an enlightening problem: What is the probability that a needle thrown randomly on a ground made of equispaced parallel strips lies on two of them? In this work, we show that the solution to this problem, and its generalization to dimensions, allows us to discover a quantized form of the Johnson-Lindenstrauss (JL) Lemma, i.e., one that combines a linear dimensionality reduction procedure with a uniform quantization of precision . In particular, given a finite set of points and a distortion level , as soon as , we can (randomly) construct a mapping from to that approximately preserves the pairwise distances between the…
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