学习数据分类：线性解码适用于压缩型MRI
Learning Data Triage: Linear Decoding Works for Compressive MRI
摘要：
压缩采样的标准方法考虑恢复具有某些已知结构的未知确定性信号，并基于已知结构设计子采样模式和恢复算法。这种方法需要寻找揭示信号结构的良好表示，并解决非光滑凸最小化问题（例如，基础追求）。在本文中，另一种方法被认为是：我们学习一个很好的子采样模式的基础上，现有的训练信号，而不知道的信号结构提前，并重建相应的子采样信号的计算成本低得多的线性重建。我们提供了一个理论上的保证恢复错误，并通过实验显示在现实世界的MRI数据的有效性，建议压缩MRI计划。
英文摘要：
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and solving a non-smooth convex minimization problem (e.g., basis pursuit). In this paper, another approach is considered: We learn a good sub-sampling pattern based on available training signals, without knowing the signal structure in advance, and reconstruct an accordingly sub-sampled signal by computationally much cheaper linear reconstruction. We provide a theoretical guarantee on the recovery error, and show via experiments on real-world MRI data the effectiveness of the proposed compressive MRI scheme.
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