  We consider the scenario where one observes an outcome variable and sets of
features from multiple assays, all measured on the same set of samples. One
approach that has been proposed for dealing with this type of data is ``sparse
multiple canonical correlation analysis'' (sparse mCCA). All of the current
sparse mCCA techniques are biconvex and thus have no guarantees about reaching
a global optimum. We propose a method for performing sparse supervised
canonical correlation analysis (sparse sCCA), a specific case of sparse mCCA
when one of the datasets is a vector. Our proposal for sparse sCCA is convex
and thus does not face the same difficulties as the other methods. We derive
efficient algorithms for this problem, and illustrate their use on simulated
and real data.
