Practical graph signal sampling with log-linear size scaling
Practical graph signal sampling with log-linear size scaling
复制标题
DOI：
10.1016/j.sigpro.2021.108436
复制
发表时间：
2021-02
期刊：
影响因子：
--
通讯作者：
Ajinkya Jayawant;Antonio Ortega
中科院分区：
文献类型：
--
作者：
Ajinkya Jayawant;Antonio Ortega
Please try later.
Graph signal sampling is the problem of selecting a subset of representative graph vertices whose values can be used to interpolate missing values on the remaining graph vertices. Optimizing the choice of sampling set using concepts from experiment design can help minimize the effect of noise in the input signal. While many existing sampling set selection methods are computationally intensive because they require an eigendecomposition, existing eigendecomposition-free methods are still much slower than random sampling algorithms for large graphs. In this paper, through optimizing sampling sets towards the D-optimal objective from experiment design, we propose a sampling algorithm that has complexity comparable to random sampling algorithms, while reaching accuracy similar to existing eigendecomposition-free methods for a broad range of graph types.