Large deviation principles induced by the Stiefel manifold, and random multi-dimensional projections

Given an $n$-dimensional random vector $X^{(n)}$ , for $k < n$, consider its $k$-dimensional projection $mathbf{a}_{n,k}X^{(n)}$, where $mathbf{a}_{n,k}$ is an $n times k$-dimensional matrix belonging to the Stiefel manifold $mathbb{V}_{n,k}$ of orthonormal $k$-frames in $mathbb{R}^n$. For a class of sequences ${X^{(n)}}$ that includes the uniform distributions on scaled $ell_p^n$ balls, $p in (1,infty]$, and product measures with sufficiently light tails, it is shown that the sequence of projected vectors ${mathbf{a}_{n,k}^intercal X^{(n)}}$ satisfies a large deviation principle whenever the empirical measures of the rows of $sqrt{n} mathbf{a}_{n,k}$ converge, as $n rightarrow infty$, to a probability measure on $mathbb{R}^k$. In particular, when $mathbf{A}_{n,k}$ is a random matrix drawn from the Haar measure on $mathbb{V}_{n,k}$, this is shown to imply a large deviation principle for the sequence of random projections ${mathbf{A}_{n,k}^intercal X^{(n)}}$ in the quenched sense (that is, conditioned on almost sure realizations of ${mathbf{A}_{n,k}}$). Moreover, a variational formula is obtained for the rate function of the large deviation principle for the annealed projections ${mathbf{A}_{n,k}^intercal X^{(n)}}$, which is expressed in terms of a family of quenched rate functions and a modified entropy term. A key step in this analysis is a large deviation principle for the sequence of empirical measures of rows of $sqrt{n} mathbf{A}_{n,k}$, which may be of independent interest. The study of multi-dimensional random projections of high-dimensional measures is of interest in asymptotic functional analysis, convex geometry and statistics. Prior results on quenched large deviations for random projections of $ell_p^n$ balls have been essentially restricted to the one-dimensional setting.