Approximation of projections of random vectors

Details Approximation of projections of random vectors Approximation of projections of random vectors

2009-12-10

arxiv.org/abs/0912.2044v4

Mathematics

Probability

Typo in abstract corrected; $k=c\sqrt{\log(d)}$, not $c\log(d)$. To appear in JOTP

Scientific paper

Let $X$ be a $d$-dimensional random vector and $X_\theta$ its projection onto the span of a set of orthonormal vectors $\{\theta_1,...,\theta_k\}$. Conditions on the distribution of $X$ are given such that if $\theta$ is chosen according to Haar measure on the Stiefel manifold, the bounded-Lipschitz distance from $X_\theta$ to a Gaussian distribution is concentrated at its expectation; furthermore, an explicit bound is given for the expected distance, in terms of $d$, $k$, and the distribution of $X$, allowing consideration not just of fixed $k$ but of $k$ growing with $d$. The results are applied in the setting of projection pursuit, showing that most $k$-dimensional projections of $n$ data points in $\R^d$ are close to Gaussian, when $n$ and $d$ are large and $k=c\sqrt{\log(d)}$ for a small constant $c$.

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