Isometry-enforcing data transformations for improving sparse model learning Academic Article uri icon


  • Abstract. Imposing sparsity constraints (such as l1-regularization) on the model parameters is a practical and efficient way of handling very high-dimensional data, which also yields interpretable models due to embedded feature-selection. Compressed sensing (CS) theory provides guarantees on the quality of sparse signal (in our case, model) reconstruction that relies on the so-called restricted isometry property (RIP) of the sensing (design) matrices. This, however, cannot be guaranteed as these matrices form a subset of the underlying …

publication date

  • January 1, 2009