Highly Nonlinear Approximations for Sparse Signal Representation
Gravitational Sound clips are considered within the particular context of data reduction. It is shown that these types of signals can be approximated at high quality using much less elementary components than those required within the standard orthogonal basis framework. Furthermore, a measure a local sparsity is shown to render meaningful information about the variation of a signal along time, by generating a set of local sparsity values which is much smaller than the dimension of the signal.
by Laura Rebollo-Neira and Angel Plastino