Column randomization and almost-isometric embeddings

TL;DR

This paper demonstrates that column randomization of $( ext{} ext{delta},k ext{)}$-regular matrices yields near-isometric embeddings for arbitrary sets, with optimal probability bounds and applications to high-dimensional geometry and compressed sensing.

Contribution

It introduces a method to achieve almost-isometric embeddings by column randomization of regular matrices, providing optimal probabilistic guarantees.

Findings

Randomized column signs induce gaussian-like behavior.

Embedding error bounds depend on gaussian mean-width and matrix regularity.

Results are optimal for small delta values.

Abstract

The matrix $A:\mathbb{R}^n \to \mathbb{R}^m$ is $(\delta,k)$-regular if for any $k$-sparse vector $x$, $$ \left| \|Ax\|_2^2-\|x\|_2^2\right| \leq \delta \sqrt{k} \|x\|_2^2. $$ We show that if $A$ is $(\delta,k)$-regular for $1 \leq k \leq 1/\delta^2$, then by multiplying the columns of $A$ by independent random signs, the resulting random ensemble $A_\epsilon$ acts on an arbitrary subset $T \subset \mathbb{R}^n$ (almost) as if it were gaussian, and with the optimal probability estimate: if $\ell_*(T)$ is the gaussian mean-width of $T$ and $d_T=\sup_{t \in T} \|t\|_2$, then with probability at least $1-2\exp(-c(\ell_*(T)/d_T)^2)$, $$ \sup_{t \in T} \left| \|A_\epsilon t\|_2^2-\|t\|_2^2 \right| \leq C\left(\Lambda d_T \delta\ell_*(T)+(\delta \ell_*(T))^2 \right), $$ where $\Lambda=\max\{1,\delta^2\log(n\delta^2)\}$. This estimate is optimal for $0<\delta \leq 1/\sqrt{\log n}$.