论文标题
通过概率密度比加权的图形滤波器传输
Graph Filter Transfer via Probability Density Ratio Weighting
论文作者
论文摘要
恢复图形信号的问题是图形信号处理的主要主题之一。该问题的代表性方法是图形Wiener滤波器，它利用从历史数据计算的目标信号的统计信息来构建有效的估计器。但是，我们经常遇到情况，在这种情况下，由于拓扑变化，当前图与历史数据的图形有所不同，从而导致估算器的性能下降。本文提出了一种图形滤波器传输方法，该方法从拓扑变化下的历史数据中学习了有效的估计器。所提出的方法利用了当前和历史观察的概率密度比，并构建了一个估计器，该估计值可以最大程度地减少当前图域中的重建误差。合成数据的实验表明，所提出的方法优于其他方法。
The problem of recovering graph signals is one of the main topics in graph signal processing. A representative approach to this problem is the graph Wiener filter, which utilizes the statistical information of the target signal computed from historical data to construct an effective estimator. However, we often encounter situations where the current graph differs from that of historical data due to topology changes, leading to performance degradation of the estimator. This paper proposes a graph filter transfer method, which learns an effective estimator from historical data under topology changes. The proposed method leverages the probability density ratio of the current and historical observations and constructs an estimator that minimizes the reconstruction error in the current graph domain. The experiment on synthetic data demonstrates that the proposed method outperforms other methods.
