🤖 AI Summary
本文探讨了随机投影在保持几何结构方面的局限性，通过分析Johnson-Lindenstrauss引理，并采用线性草图与奇异值分解方法来恢复距离特征。
📝 Abstract
The Johnson-Lindenstrauss (JL) lemma guarantees that a random projection of $n$ points to
$m=O(\varepsilon^{-2}\log n)$ dimensions preserves pairwise squared distances within relative
error $\varepsilon$ with high probability, and this dimension order is asymptotically
optimal. In high dimensions, however, distances concentrate around a baseline while key
geometric information lies in much smaller fluctuations. We show that the JL bound can
therefore be uninformative about retained geometry: an independent Gaussian replacement map
can satisfy it even though the replacement cloud is independent of the original data.
We then ask how well any decoder can recover a feature $f(D)$ of a squared distance $D$ from
a linear sketch. Under squared-error loss, the optimal decoder is conditional expectation, so
recovery defines a linear operator whose singular values quantify feature recovery. For
isotropic Gaussian data ($Σ=σ^2 I_d$), we diagonalize this operator in closed form.
For fixed $k$ with $m,d-m\to\infty$, its $k$th singular value satisfies $\ell_k\approx(m/
d)^{k/2}$.
This yields three sharp consequences. A rank-$m$ sketch retains at most an $m/d$ fraction of
the variance of any feature of one squared distance. If $m\to\infty$ and $m/d\to0$, the
expected Kendall correlation is $\frac{2}π\sqrt{m/d}(1+o(1))$; for fixed $q$, nearest-
neighbor agreement tends to $1/q$. Yet one projection can satisfy the JL bound while mean
Kendall correlation vanishes when $\log n\ll m\ll d$. After removing scale, Haar-averaged
retained covariance-shape information is $(m/d)^2$. Thus JL distance preservation does not
quantify the geometry available for comparison or inference.