Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals
Pattern-coupled sparse Bayesian learning for recovery of block-sparse signals
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DOI：
10.1109/icassp.2014.6853928
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Yanning Shen;Huiping Duan;Jun Fang;Hongbin Li
中科院分区：
文献类型：
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作者：
Yanning Shen;Huiping Duan;Jun Fang;Hongbin Li
本文提出了一种新的稀疏贝叶斯学习方法，用于恢复具有未知聚类模式的块稀疏信号。引入一种模式耦合的分层高斯先验模型来表征系数之间的统计相关性，并采用一组超参数来控制信号系数的稀疏性。与传统的稀疏贝叶斯学习框架中每个单独的超参数与每个系数独立关联不同，本文中每个系数的先验不仅涉及其自身的超参数，还涉及其近邻的超参数。通过这种方式，相邻系数的稀疏模式相互关联，分层模型具有鼓励结构化稀疏解决方案的潜力。通过期望最大化（EM）算法最大化后验概率来学习超参数和稀疏信号。
In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. A pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, where a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm.