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Found 6 papers, 0 papers with code
Adaptive Combination of l0 LMS Adaptive Filters for Sparse System Identification in Fluctuating Noise Power
Recently, the l0-least mean square (l0-LMS) algorithm has been proposed to identify sparse linear systems by employing a sparsity-promoting continuous function as an approximation of l0 pseudonorm penalty.
Performance Analysis of the Gradient Comparator LMS Algorithm
The sparsity-aware zero attractor least mean square (ZA-LMS) algorithm manifests much lower misadjustment in strongly sparse environment than its sparsity-agnostic counterpart, the least mean square (LMS), but is shown to perform worse than the LMS when sparsity of the impulse response decreases.
Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm
The performance analysis of the proposed multi-task diffusion APA algorithm is studied in mean and mean square sense.
Sparse Distributed Learning via Heterogeneous Diffusion Adaptive Networks
In-network distributed estimation of sparse parameter vectors via diffusion LMS strategies has been studied and investigated in recent years.
Modified Hard Thresholding Pursuit with Regularization Assisted Support Identification
Hard thresholding pursuit (HTP) is a recently proposed iterative sparse recovery algorithm which is a result of combination of a support selection step from iterated hard thresholding (IHT) and an estimation step from the orthogonal matching pursuit (OMP).
A Two Stage Generalized Block Orthogonal Matching Pursuit (TSGBOMP) Algorithm
Furthermore, assuming real Gaussian sensing matrix entries, we find a lower bound on the probability that the derived recovery bounds are satisfied.
