.png){kind=icon}
返回
Adaptive Bayesian Estimation with Cluster Structured Sparsity
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2015.2477440.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Armed with structures, group sparsity can be exploited to extraordinarily improve the performance of adaptive estimation. In this letter, the adaptive estimation algorithm for cluster structured sparse signals, called A-CluSS, is proposed. In particular, a hierarchical Bayesian model is built, where both sparse prior and cluster structured prior are exploited simultaneously. The adaptive updating formulas for statistical variables are obtained via the variational Bayesian inference and the resulted algorithms can adaptively estimate the cluster structured sparse signals without knowledge of block size, block numbers and block locations. Superiority of proposed A-CluSS is demonstrated via various simulations.
Keyword:
Adaptive estimation
Bayesian inference
block sparsity
cluster structured sparsity
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
Bayesian compressive sensing for cluster structured sparse signals簇结构稀疏信号的贝叶斯压缩感知.png)
SIGNAL PROCESSING
OAAI
SIGNAL PROCESSINGIF3.6
OAAI分享收藏分享收藏
查看更多内容