Motivated by the problem of identifying correlations between genes or
features of two related biological systems, we propose a model of \emph{feature
selection} in which only a subset of the predictors $X_t$ are dependent on the
multidimensional variate $Y$, and the remainder of the predictors constitute a
"noise set" $X_u$ independent of $Y$.