AI RESEARCH
Robust Sparse Signal Recovery with Outliers: A Hard Thresholding Pursuit Approach Based on LAD
arXiv CS.CV
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ArXi:2601.06558v2 Announce Type: replace-cross Recovering a sparse signal from outlier-contaminated measurements is a fundamental challenge in many applications. While existing algorithms predominantly address scenarios with bounded noise or assume known signal sparsity, few methods tackle the practical problem of sparse recovery from gross outliers without prior knowledge of sparsity. To bridge this gap, we study the sparsity-constrained Least Absolute Deviations (LAD) minimization problem.