  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 $0<\delta \leq 1/\sqrt{\log n}$.
