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Diagonalizable Shift and Filters for Directed Graphs Based on the Jordan-Chevalley Decomposition

机译:基于Jordan-Chevalley分解的有向图的对角化移位和滤波

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Graph signal processing on directed graphs poses theoretical challenges since an eigendecomposition of filters is in general not available. Instead, Fourier analysis requires a Jordan decomposition and the frequency response is given by the Jordan normal form, whose computation is numerically unstable for large sizes. In this paper, we propose to replace a given adjacency shift A by a diagonalizable shift AD obtained via the Jordan-Chevalley decomposition. This means, as we show, that AD generates the subalgebra of all diagonalizable filters and is itself a polynomial in A (i.e., a filter). For several synthetic and real-world graphs, we show how AD adds and removes edges compared to A.
机译:有向图上的图信号处理提出了理论上的挑战,因为通常无法使用滤波器的特征分解。取而代之的是,傅立叶分析需要Jordan分解,并且频率响应由Jordan范式给出,该范式的计算对于大尺寸而言在数值上不稳定。在本文中,我们建议用对角线化位移A替换给定的邻接度位移A D 通过Jordan-Chevalley分解获得。正如我们所示,这意味着A D 生成所有可对角线滤波器的子代数,它本身是A(即滤波器)中的多项式。对于几个合成图和真实图,我们展示了A D 与A相比,添加和删除边缘。

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