Bayesian compressive sensing for cluster structured sparse signals
Bayesian compressive sensing for cluster structured sparse signals
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DOI：
10.1016/j.sigpro.2011.07.015
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Lei Yu;Hong Sun;J. Barbot;G. Zheng
中科院分区：
文献类型：
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作者：
Lei Yu;Hong Sun;J. Barbot;G. Zheng
在传统的压缩感知（CS）框架中，仅采用信号在时域或频域上的稀疏先验来保证精确的逆恢复。除了稀疏先验之外，信号的稀疏模式上的结构也被用作附加先验，称为基于模型的压缩感知，例如小波系数上的聚类结构和树结构。本文研究了簇结构稀疏信号。在贝叶斯压缩感知的框架下，采用分层贝叶斯模型对稀疏先验和聚类先验进行建模，并采用马尔可夫链蒙特卡罗（MCMC）抽样进行推理。不同于国家的最先进的算法，这也是考虑到集群之前，该算法解决了反问题自动先验信息的集群的数量和每个集群的大小是未知的。实验结果表明，该算法优于许多国家的最先进的算法。
In traditional framework of compressive sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called model-based compressive sensing, such as clustered structure and tree structure on wavelet coefficients. In this paper, the cluster structured sparse signals are investigated. Under the framework of Bayesian compressive sensing, a hierarchical Bayesian model is employed to model both the sparse prior and cluster prior, then Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. Unlike the state-of-the-art algorithms which are also taking into account the cluster prior, the proposed algorithm solves the inverse problem automatically—prior information on the number of clusters and the size of each cluster is unknown. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms.