In the rare situation in which a sparsity level was unreachable (by modifying the regularization parameter) the inference accuracy was assumed to be the average of the accuracies from the adjacent sparsity levels.
In [10], a sparsity as well as a ridge-penalized, non-negativity constrained, ordinary least squares method is used to estimate the spatial rainfall map from linear path-averaged rainfall intensities, albeit for a single snapshot.
In EESN, different ESNs are generally independent of each other, so we can first reduce the EESN model to a single ESN model by setting N ESN = 1 and optimize the ESN model parameters (a in, a res, f sparsity, α, β, and d S ), by using grid search.
The fundamental premise in CS is that certain classes of signals, such as natural images, have a succinct representation in terms of a sparsity inducing basis, or frame, such that only a few coefficients are significant and the remaining coefficients are negligibly small.
In [19], a sparsity-enhancing algorithm is discussed for compressive estimation of doubly selective multicarrier channels, while the leakage effect is discussed but not for massive MIMO.
In [3], a sparsity-driven algorithm to generate high-resolution SA-ISAR images is given, in which a SA-ISAR imaging problem is converted into a sparsity-constrained optimization problem based on the maximum a posteriori estimation.
The reconstruction of these structured signals is usually performed with an iterative least-squares optimization in which a sparsity-promoting norm serves as an additional constraint, or regularizer.
Fig. 2 a Sparsity in spatial channel and b uplink and downlink supports with angle deviation.
The number of targets can also be jointly estimated in this process using a sparsity constraint.
In this paper, we propose a sparsity based algorithm for target detection in hyperspectral images.
