Adaptive compressive measurements can offer significant system performance advantages due to online learning over non-adaptive or static compressive measurements for a variety of applications, such as image formation and target identification. However, such adaptive measurements tend to be sub-optimal due to their greedy design. Here, we propose a non-greedy adaptive compressive measurement design framework and analyze its performance for a face recognition task. While a greedy adaptive design aims to optimize the system performance on the next immediate measurement, a non-greedy adaptive design goes beyond that by strategically maximizing the system performance over all future measurements. Our non-greedy adaptive design pursues a joint optimization of measurement design and photon allocation within a rigorous information-theoretic framework. For a face recognition task, simulation studies demonstrate that the proposed non-greedy adaptive design achieves a nearly two to three fold lower probability of misclassification relative to the greedy adaptive and static designs. The simulation results are validated experimentally on a compressive optical imager testbed. c 2016 Optical Society of America
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机译：自适应压缩测量可以提供显着的系统性能优势，这是因为在线学习优于非自适应或静态压缩测量，适用于各种应用，例如图像形成和目标识别。然而，由于它们的贪婪设计，这样的自适应测量趋于次优。在这里，我们提出了一种非贪婪的自适应压缩测量设计框架，并分析了其在人脸识别任务中的性能。贪婪的自适应设计旨在在下一次立即测量时优化系统性能，而非贪婪的自适应设计则超越了这一范围，其策略是在所有未来的测量中最大化系统性能。我们的非贪婪自适应设计在严格的信息理论框架内追求测量设计和光子分配的联合优化。对于人脸识别任务，仿真研究表明，相对于贪婪的自适应和静态设计，所提出的非贪婪的自适应设计实现了近2到3倍的错误分类概率。仿真结果在压缩光学成像仪试验台上进行了实验验证。 c 2016美国眼镜学会
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