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Knowledge-Aided Normalized Iterative Hard Thresholding Algorithms and Applications to Sparse Reconstruction

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arxiv 1809.09281 v1 pith:VP3JAFLB submitted 2018-09-25 cs.IT math.IT

R. C. de Lamare This is my paper · ORCID
classification cs.ITmath.IT
keywords algorithmalgorithmsapplicationsharditerativeka-nihtknowledge-aidednormalized
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This paper deals with the problem of sparse recovery often found in compressive sensing applications exploiting a priori knowledge. In particular, we present a knowledge-aided normalized iterative hard thresholding (KA-NIHT) algorithm that exploits information about the probabilities of nonzero entries. We also develop a strategy to update the probabilities using a recursive KA-NIHT (RKA-NIHT) algorithm, which results in improved recovery. Simulation results illustrate and compare the performance of the proposed and existing algorithms.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Normalized Iterative Hard Thresholding for Tensor Recovery

    cs.LG 2025-07 reject novelty 4.0 of 10

    A claimed tensor NIHT algorithm is in practice a hard-thresholded SVRG method whose promised convergence theorem is not actually proved.

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