“Group
Sparse RLS Algorithms” Home
E.M. Eksioglu,
Group Sparse RLS Algorithms, International Journal of Adaptive Control and
Signal Processing, accepted for publication, 2014.
MATLAB code to generate the group sparsity based
RLS algorithm curves:
Abstract:
Group sparsity is one of the important signal
priors for regularization of inverse problems. Sparsity with group structure is
encountered in numerous applications. However, despite the abundance of
sparsity-based adaptive algorithms, attempts at group sparse adaptive methods
are very scarce. In this paper, we introduce novel recursive least squares
(RLS) adaptive algorithms regularized via penalty functions, which promote
group sparsity. We present a new analytic approximation for ℓp,0 norm
to utilize it as a group sparse regularizer. Simulation results confirm the
improved performance of the new group sparse algorithms over regular and sparse
RLS algorithms when group sparse structure is present.