论文标题
图形信号处理符合盲源分离
Graph Signal Processing Meets Blind Source Separation
论文作者
论文摘要
在图形信号处理（GSP）中，对信号中依赖项的先验信息收集在图中，然后在处理或分析信号时使用该信息。在不同域中已经开发和分析了盲源分离（BSS）技术，但是对于图形信号，对BSS的研究仍处于起步阶段。在本文中，这个差距充满了两个贡献。首先，与GSP框架相关的非参数BSS方法是完善的，Cramér-rao绑定（CRB）用于混合和混合矩阵估计器，在高斯移动平均值信号的情况下，以及用于CRB的可实现的GAUSS的新参数方法，是Quuss Spemant的平均值介绍的平均值。其次，我们还考虑了非高斯图信号的BS，并提出了两种方法。可识别性条件表明，利用图结构和非高斯性的性能提供了一种比仅基于图形依赖性或非高斯性的方法更强大的方法。数值研究还证明，所提出的方法在分离非高斯图信号方面更有效。
In graph signal processing (GSP), prior information on the dependencies in the signal is collected in a graph which is then used when processing or analyzing the signal. Blind source separation (BSS) techniques have been developed and analyzed in different domains, but for graph signals the research on BSS is still in its infancy. In this paper, this gap is filled with two contributions. First, a nonparametric BSS method, which is relevant to the GSP framework, is refined, the Cramér-Rao bound (CRB) for mixing and unmixing matrix estimators in the case of Gaussian moving average graph signals is derived, and for studying the achievability of the CRB, a new parametric method for BSS of Gaussian moving average graph signals is introduced. Second, we also consider BSS of non-Gaussian graph signals and two methods are proposed. Identifiability conditions show that utilizing both graph structure and non-Gaussianity provides a more robust approach than methods which are based on only either graph dependencies or non-Gaussianity. It is also demonstrated by numerical study that the proposed methods are more efficient in separating non-Gaussian graph signals.
