Sparse recovery using expanders via hard thresholding algorithm
KK Wen, JX He, P Li - Signal Processing, 2025 - Elsevier
Expanders play an important role in combinatorial compressed sensing. Via expanders
measurements, we propose the expander normalized heavy ball hard thresholding …
measurements, we propose the expander normalized heavy ball hard thresholding …
Convergence of projected subgradient method with sparse or low-rank constraints
Many problems in data science can be treated as recovering structural signals from a set of
linear measurements, sometimes perturbed by dense noise or sparse corruptions. In this …
linear measurements, sometimes perturbed by dense noise or sparse corruptions. In this …
Matrix recovery from nonconvex regularized least absolute deviations
J Xu, P Li, B Zheng - Inverse Problems, 2024 - iopscience.iop.org
Matrix recovery from nonconvex regularized least absolute deviations - IOPscience Skip to
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Iterative Difference Hard-Thresholding Algorithm for Sparse Signal Recovery
A Cui, H He, Z **e, W Yan… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In this paper, a nonconvex surrogate function, namely, Laplace norm, is studied to recover
the sparse signals. Firstly, we discuss the equivalence of the optimal solutions of-norm …
the sparse signals. Firstly, we discuss the equivalence of the optimal solutions of-norm …
Robust Bilinear form identification: A subgradient method with geometrically decaying stepsize in the presence of heavy-tailed noise
G Yang - IEICE Transactions on Communications, 2024 - ieeexplore.ieee.org
This paper delves into the utilisation of the subgradient method with geometrically decaying
stepsize for Bilinear Form Identification. We introduce the iterative Wiener Filter, an l 2 …
stepsize for Bilinear Form Identification. We introduce the iterative Wiener Filter, an l 2 …