Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer
Efficient Tracking of Sparse Signals via an Earth Mover's Distance Dynamics Regularizer
复制标题
通过推土机距离动态正则化器有效跟踪稀疏信号
DOI：
10.1109/lsp.2020.3001760
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发表时间：
2018
影响因子：
3.9
通讯作者：
C. Rozell
中科院分区：
文献类型：
--
作者：
Nicholas P. Bertrand;Adam S. Charles;John Lee;Pavel Dunn;C. Rozell
跟踪算法，如卡尔曼滤波器的目的是提高推理性能，通过利用流观测的时间动态。然而，跟踪正则化往往是基于$\ell _p$-范数，不能考虑相邻信号元素之间的重要几何关系。我们提出了一种实用的方法，通过推土机距离动态滤波（EMD-DF）算法使用推土机距离（EMD），当系数空间存在应遵守的自然几何形状时（例如，有意义的排序）。具体来说，这封信提出了一个新的贝克曼公式，大大降低了计算的复杂性，以及评估的性能和复杂性的成像和频率跟踪应用程序的建议的方法与真实的和模拟的神经生理学数据。
Tracking algorithms such as the Kalman filter aim to improve inference performance by leveraging the temporal dynamics in streaming observations. However, the tracking regularizers are often based on the $\ell _p$-norm which cannot account for important geometrical relationships between neighboring signal elements. We propose a practical approach to using the earth mover's distance (EMD) via the earth mover's distance dynamic filtering (EMD-DF) algorithm for causally tracking time-varying sparse signals when there is a natural geometry to the coefficient space that should be respected (e.g., meaningful ordering). Specifically, this letter presents a new Beckmann formulation that dramatically reduces computational complexity, as well as an evaluation of the performance and complexity of the proposed approach in imaging and frequency tracking applications with real and simulated neurophysiology data.