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Exact Limits of Random Projections for Preserving Geometry: Distance Recovery, Nearest-Neighbor Rankings, and Covariance Shape in Gaussian Models

arXiv cs.LG ↗ · 2026-09-03 Cached

This paper shows that the Johnson–Lindenstrauss lemma can be uninformative about retained geometry in high dimensions and derives exact limits for features like distance recovery, nearest-neighbor rankings, and covariance shape in Gaussian models.

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