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.

翻译：恢复图形信号的问题是图形信号处理过程中的主要问题之一。 解决这一问题的一种有代表性的方法是图形维纳过滤器,该过滤器利用从历史数据中计算的目标信号的统计资料来构建一个有效的估测器。然而,我们经常遇到的情况是,由于地形变化,当前图表与历史数据不同,导致估测器的性能退化。本文提议了一个图形过滤传输方法,从地形变化下的历史数据中学习一个有效的估测器。拟议方法利用了当前和历史观测的概率密度比率,并构建了一个估计器,以尽量减少当前图表域的重建错误。关于合成数据的实验表明,拟议方法优于其他方法。