多测量中的结构稀疏性
数值分析
2022-01-03 v2 数值分析
摘要
我们针对多测量向量（MMV）设定下的分布式压缩感知提出了一种新颖的稀疏性模型。我们的模型将行稀疏性的概念推广，以允许在诸如地震勘探和无损检测等多种应用中出现的更一般类型的结构化稀疏性。为从观测测量中重建结构化数据，我们推导出一个非凸但良态的LASSO型泛函。通过利用该泛函的凸-凹几何结构，我们设计了一种投影梯度下降算法，并通过在合成与真实数据上的大量数值模拟展示了其有效性。
引用
@article{arxiv.2103.01908,
title = {Structural Sparsity in Multiple Measurements},
author = {Florian Boßmann and Sara Krause-Solberg and Johannes Maly and Nada Sissouno},
journal= {arXiv preprint arXiv:2103.01908},
year = {2022}
}
备注
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