  The problem of quickest detection of a change in the distribution of a
$n\times p$ random matrix based on a sequence of observations having a single
unknown change point is considered. The forms of the pre- and post-change
distributions of the rows of the matrices are assumed to belong to the family
of elliptically contoured densities with sparse dispersion matrices but are
otherwise unknown. We propose a non-parametric stopping rule that is based on a
novel summary statistic related to k-nearest neighbor correlation between
columns of each observed random matrix. In the large scale regime of
$p\rightarrow \infty$ and $n$ fixed we show that, among all functions of the
proposed summary statistic, the proposed stopping rule is asymptotically
optimal under a minimax quickest change detection (QCD) model.
