Optimal dimension reduction bound for Euclidean point sets

From papers

Let f(n,d,ε)f(n,d,\varepsilon) denote the smallest mm such that every nn-point subset of 2d\ell_2^d can be embedded into 2m\ell_2^m with distortion at most 1+ε1+\varepsilon. Dimension-reduction conjecture. For all n,d>1n,d>1 and 0<ε<10<\varepsilon<1,

f(n,d,ε)=Θ(min{n,d,ε2lg(2+ε2n)}).f(n,d,\varepsilon)=\Theta\left(\min\left\{n,d,\varepsilon^{-2}\lg(2+\varepsilon^2n)\right\}\right).

This conjecture predicts the optimal Euclidean target dimension across the full range of distortion parameters, including the regime where ε\varepsilon approaches 1/min{n,d}1/\sqrt{\min\{n,d\}}; the paper proves a matching lower bound up to a logarithmic factor in a nearly full range of ε\varepsilon, while the stated formula remains the conjectural optimal form.

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Primary source

Kasper Green Larsen and Jelani Nelson, “Optimality of the Johnson-Lindenstrauss Lemma”, arXiv:1609.02094 (2017).

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