Compressive sensing has become a powerful addition to uncertainty quantification in recent years. We propose a new method to identify new bases for random variables through linear mappings such that the representation of the quantity of interest is more sparse with new basis functions associated with the new random variables. This sparsity increases both the efficiency and accuracy of the compressive sensing-based uncertainty quantification method. Specifically, we consider rotation-based linear mappings which are determined iteratively for Hermite polynomial expansions. We demonstrate the efficiency of this method with uncertainty quantification problems in PDEs and biomolecular systems.