Sparse Reduced-Rank Regression for Simultaneous Rank and Variable Selection via Manifold Optimization

We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as a singular value decomposition with sparse singular vectors. The traditional estimation procedure for the coefficient parameter often fails when the true rank of the parameter is hig