  Sparse representation of structured signals requires modelling strategies
that maintain specific signal properties, in addition to preserving original
information content and achieving simpler signal representation. Therefore, the
major design challenge is to introduce adequate problem formulations and offer
solutions that will efficiently lead to desired representations. In this
context, sparse representation of covariance and precision matrices, which
appear as feature descriptors or mixture model parameters, respectively, will
be in the main focus of this paper.
