  From concentration inequalities for the suprema of Gaussian or Rademacher
processes an inequality is derived. It is applied to sharpen existing and to
derive novel bounds on the empirical Rademacher complexities of unit balls in
various norms appearing in the context of structured sparsity and multitask
dictionary learning or matrix factorization. A key role is played by the
largest eigenvalue of the data covariance matrix.
