A Framework for Clustered and Skewed Sparse Signal Recovery
A Framework for Clustered and Skewed Sparse Signal Recovery
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DOI：
10.1109/tsp.2018.2839622
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发表时间：
2018-08
影响因子：
5.4
通讯作者：
Sheng Wang;Nazanin Rahnavard
中科院分区：
文献类型：
--
作者：
Sheng Wang;Nazanin Rahnavard
提出了一种新的框架--聚类偏斜正态混合信任传播，用于解决欠采样聚类信号的重构问题，其中每个聚类中的信号系数的大小是不对称分布的。为了解决偏度特征，利用有限的偏态-正态混合密度对先验分布进行建模，其中信号的边缘后验由一种有效的基于消息传递的近似算法推断。提出了一种基于期望最大化的混合密度估计算法。然后利用Potts模型对聚类属性进行建模，并设计了一种循环信任传播算法来提升空间特征。实验结果表明，该方法在充分利用信号的聚集性和非对称性方面具有较高的效率和较好的性能。
A novel framework, clustered-skew normal mixture-belief propagation, is developed to solve the reconstruction of undersampled clustered signals, where the magnitudes of signal coefficients in each cluster are distributed asymmetrically w.r.t the cluster mean. To address the skewness feature, a finite skew-normal density mixture is utilized to model the prior distribution, where the marginal posterior of the signal is inferred by an efficient approximate message-passing-based algorithm. An expectation-maximization-based algorithm is developed to estimate the mixture density. The clustered property is then modeled by the Potts model, and a loopy belief propagation algorithm is designed to promote the spatial feature. Experimental results show that our technique is highly effective and efficient in exploiting both the clustered feature and asymmetrical feature of the signals and outperforms many sophisticated techniques.