多重测量中的结构稀疏性
Structural Sparsity in Multiple Measurements
摘要：
提出了一种新的稀疏模型，用于多测量向量（MMV）环境下的分布式压缩感知。我们的模型扩展了行稀疏性的概念，以允许在各种应用中出现更一般类型的结构化稀疏性，例如，地震勘探和无损检测。为了重建结构化的数据，从观察到的测量，我们推导出一个非凸的，但条件良好的LASSO型功能。通过利用凹凸几何的功能，我们设计了一个投影梯度下降算法，并在广泛的数值模拟，玩具和真实数据显示其有效性。
英文摘要：
We propose a novel sparsity model for distributed compressed sensing in the multiple measurement vectors (MMV) setting. Our model extends the concept of row-sparsity to allow more general types of structured sparsity arising in a variety of applications like, e.g., seismic exploration and non-destructive testing. To reconstruct structured data from observed measurements, we derive a non-convex but well-conditioned LASSO-type functional. By exploiting the convex-concave geometry of the functional, we design a projected gradient descent algorithm and show its effectiveness in extensive numerical simulations, both on toy and real data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
