Sparse Recovery

Latest papers 68

8 papers in the last four weeks, down 20% on the four weeks before. 0.0% of all new papers.

The Effective Number of Nonzeros: Theory and Regularization for Sparse Recovery
Mar 14, 2026Haoyu He, Hao Wang, Jiashan Wang +1Sparse Recovery Entropy Regularization
School of Information Science and Technology, ShanghaiTech University, Shanghai, China · Department of Mathematics, University of Washington, Seattle, WA, USA · School of Computer Science and Engineering, Sun Yat-sen University
Variational Garrote for Sparse Inverse Problems
Mar 13, 2026Kanghun Lee, Hyungjoon Soh, Junghyo JoSparse Recovery Variational Inference
Department of Science Education, Seoul National University, Seoul, 08826, Korea · Center for Theoretical Physics and Artificial Intelligence Institute, Seoul National University, Seoul, 08826, Korea · School of Computational Sciences, Korea Institute for Advanced Study, Seoul, 02455, Korea
Online Sparse Synthetic Aperture Radar Imaging
Mar 9, 2026Conor Flynn, Radoslav Ivanov, Birsen YaziciSynthetic Aperture Radar Streaming Algorithms
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180 USA · Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180 USA
Step-Size Decay and Structural Stagnation in Greedy Sparse Learning
Mar 8, 2026Pablo M. BernáSparse Recovery Learning Rate Scheduling
Departamento de Matemáticas, CUNEF Universidad Madrid, Spain
Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Feb 25, 2026Joshua Schulz, David Schote, Christoph Kolbitsch +2Accelerated MRI Reconstruction Sparse Recovery
Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany · School of Mathematical Sciences, Queen Mary University of London, UK
Separating Oblivious and Adaptive Models of Variable Selection
Feb 18, 2026Ziyun Chen, Jerry Li, Kevin Tian +1Sparse Recovery Feature Selection
University of Washington · University of Texas at Austin
Robustness to Sparse Adversarial Corruption in Arbitrary Linear Measurements: Beyond Exact Recovery
Oct 28, 2025Vishal Halder, Alexandre Reiffers-Masson, Abdeldjalil Aïssa-El-Bey +1Sparse Recovery Adversarial Robustness
IMT Atlantique, Lab-STICC, CNRS UMR 6285, Brest, France · Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, India
The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements
Sep 1, 2025Youssef Chaabouni, David GamarnikSparse Recovery Sample Complexity
Massachusetts Institute of Technology

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The Effective Number of Nonzeros: Theory and Regularization for Sparse Recovery

Haoyu He, Hao Wang, Jiashan Wang, Qiankun Shi

School of Information Science and Technology, ShanghaiTech University, Shanghai, China · Department of Mathematics, University of Washington, Seattle, WA, USA · School of Computer Science and Engineering, Sun Yat-sen University

Sparse Recovery Entropy Regularization

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Classical sparse recovery treats all nonzero entries equally, though numerical noise often creates long tails of negligible coefficients. This paper develops an entropy-based notion of effective sparsity to measure the coefficients carrying significant mass. The central quantity, the effective number of nonzeros (ENZ), is obtained by exponentiating the Shannon entropy of the normalized magnitude distribution. We show that ENZ decomposes exactly into the support cardinality multiplied by a distributional efficiency factor, thereby making precise its relation to the

\ell_0

count and explaining how it discounts uninformative coefficients. Furthermore, the Shannon ENZ is embedded into a parallel Rényi family that recovers several scale-invariant sparsity measures, including the

\ell_1/\ell_2

ratio, as special cases. We then prove a stability result under a restricted isometry condition, establishing an explicit bound that depends on the tail energy, measurement perturbation, and restricted isometry constant. For computation, a separable unnormalized entropy surrogate is introduced to avoid global coupling. Numerical experiments on sparse signal recovery and gradient-domain image denoising demonstrate that the resulting regularizer is robust, computationally efficient, and competitive with standard sparsity penalties.