论文标题
用蒙特卡洛在图上平滑复合物值信号
Smoothing complex-valued signals on Graphs with Monte-Carlo
论文作者
论文摘要
我们基于最近研究的确定点过程（DPP）引入了图形上复杂信号的新平滑估计器。这些估计器是由根据此DPP绘制的边和节点的子集构建的，它们组成了树和独立的，即完全包含一个周期的连接组件。我们提供了这些估计量的朱莉娅实施，并在应用于排名问题时研究其性能。
We introduce new smoothing estimators for complex signals on graphs, based on a recently studied Determinantal Point Process (DPP). These estimators are built from subsets of edges and nodes drawn according to this DPP, making up trees and unicycles, i.e., connected components containing exactly one cycle. We provide a Julia implementation of these estimators and study their performance when applied to a ranking problem.
