Greedy Sparse Learning Over Network
Greedy Sparse Learning Over Network
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DOI：
10.1109/tsipn.2017.2710905
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发表时间：
2018-09
影响因子：
3.2
通讯作者：
Ahmed Zaki;Arun Venkitaraman;S. Chatterjee;L. Rasmussen
中科院分区：
文献类型：
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作者：
Ahmed Zaki;Arun Venkitaraman;S. Chatterjee;L. Rasmussen
在这篇文章中，我们提出了一种贪婪算法，用于分布式地解决右随机网络上的稀疏学习问题。节点通过在网络上交换它们各自的中间估计的加权版本来迭代地估计稀疏信号。在存在加性噪声的情况下，我们提供了一种基于受限等距性质(RIP)的理论性能保证。在没有噪声的情况下，我们证明了在网络的每个节点上测量矩阵的RIP常数满足一定条件下，单个节点的估计集体收敛到真正的稀疏信号。此外，我们还给出了贪婪算法收敛所需迭代次数的上界。通过仿真实验表明，该算法的实际性能优于文献中的其他分布式贪婪算法。
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.