.png){kind=icon}
返回
Greedy Sparse Learning Over Network
.png){kind=icon}
PRE
AI
DOI：10.1109/TSIPN.2017.2710905.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we develop a greedy algorithm for solving the problem of sparse learning over a right stochastic network in a distributed manner. The nodes iteratively estimate the sparse signal by exchanging a weighted version of their individual intermediate estimates over the network. We provide a restricted-isometry-property (RIP)-based theoretical performance guarantee in the presence of additive noise. In the absence of noise, we show that under certain conditions on the RIP-constant of measurement matrix at each node of the network, the individual node estimates collectively converge to the true sparse signal. Furthermore, we provide an upper bound on the number of iterations required by the greedy algorithm to converge. Through simulations, we also show that the practical performance of the proposed algorithm is better than other state-of-the-art distributed greedy algorithms found in the literature.
Keyword:
Adaptive estimation
compressed sensing
convergence
distributed algorithms
greedy algorithms
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:
4.9
论文数:
733
被引数:
1.9K
机构
引用论文
.png){kind=icon}
Structure development in aerogel-processed nanocrystalline alkaline earth oxides as revealed by SANS
PREAI
分享收藏分享收藏
查看更多内容