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Two papers explore sparse signal recovery techniques · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the challenges and methodologies of recovering sparse signals from indirect observations. The first paper, by Anatoli Juditsky, focuses on developing and analyzing techniques for recovering a linear image of a sparse signal without making restrictive assumptions about the sensing matrix. The second paper, by Youssef Chaabouni, investigates sufficient conditions for sparse recovery using sparse and sparsified measurements, identifying information-theoretic thresholds and computational gains. AI

IMPACT These papers contribute to foundational research in signal processing and machine learning, potentially impacting future AI model development that relies on efficient data recovery from noisy or incomplete observations.

RANK_REASON Two academic papers published on arXiv discussing signal recovery techniques.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Two papers explore sparse signal recovery techniques · 2 sources tracked

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Two academic papers published on arXiv discussing signal recovery techniques.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Anatoli Juditsky, Arkadi Nemirovski ·

    Recovering linear images of sparse signals from indirect observations

    arXiv:2609.06182v1 Announce Type: cross Abstract: In this paper, we develop and analyze techniques for recovering a linear image $Bx$ of an unknown signal $x$ from indirect noisy observation $\omega=Ax+\xi$. It is {\em a priori} known that $x\in \cX$, a given convex compact set, …

  2. arXiv cs.LG TIER_1 English(EN) · Youssef Chaabouni, David Gamarnik ·

    The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements

    arXiv:2509.01809v2 Announce Type: replace-cross Abstract: We consider the problem of support recovery for sparse binary signals from noisy linear measurements. For sparse Gaussian measurement matrices we identify sufficient conditions on the minimal sample size for maximum-likeli…

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