SAMPLING AND RECONSTRUCTION OF SIGNALS ON PRODUCT GRAPHS
SAMPLING AND RECONSTRUCTION OF SIGNALS ON PRODUCT GRAPHS
复制标题
产品图上信号的采样和重建
DOI：
10.1109/globalsip.2018.8646609
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
G. Leus
中科院分区：
文献类型：
--
作者：
Guillermo Ortiz;M. Coutiño;S. Chepuri;G. Leus
在本文中，我们考虑的问题，二次采样和重建的信号，驻留在一个产品图的顶点，如传感器网络的时间序列，基因组信号，或在社会网络中的产品评级。具体来说，我们利用底层域的产品结构和图因子中的样本节点。所提出的方案是特别有用的处理信号的大规模产品图。采样集是使用低复杂度的贪婪算法设计的，并且可以被证明是接近最优的。为了说明所开发的理论，数值实验的基础上真实的数据集提供采样的三维动态点云和主动学习的推荐系统。
In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network. Specifically, we leverage the product structure of the underlying domain and sample nodes from the graph factors. The proposed scheme is particularly useful for processing signals on large-scale product graphs. The sampling sets are designed using a low-complexity greedy algorithm and can be proven to be near-optimal. To illustrate the developed theory, numerical experiments based on real datasets are provided for sampling 3D dynamic point clouds and for active learning in recommender systems.