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We show that if μ₁, ..., μₘ are log-concave subgaussian or supergaussian probability measures in ${ℝ}^{n_i}$, i ≤ m, then for every F in the Grassmannian $G_{N,n}$, where N = n₁ + ⋯ + nₘ and n< N, the isotropic constant of the marginal of the product of these measures, ${\pi }_F\left(\mu ₁\otimes \cdots \otimes \mu ₘ\right)$, is bounded. This extends known results on bounds of the isotropic constant to a larger class of measures.

We present an approach that allows one to bound the largest and smallest singular values of an $N\times n$ random matrix with iid rows, distributed according to a measure on ${ℝ}^n$ that is supported in a relatively small ball and linear functionals are uniformly bounded in $L_p$ for some $p>8$, in a quantitative (non-asymptotic) fashion. Among the outcomes of this approach are optimal estimates of $1\pm c\sqrt{n/N}$ not only in the case of the above mentioned measure, but also when the measure is log-concave or when it a product measure...