论文标题
张量上的多路图信号处理：不规则几何分析
Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries
论文作者
论文摘要
图信号处理（GSP）是研究属于不规则结构的数据的重要方法。随着获得的数据越来越多地采用多路张量的形式，需要新的信号处理工具来最大程度地利用数据中的多路结构。在本文中，我们回顾了将GSP推广到多路数据的现代信号处理框架，从耦合到熟悉的常规轴（例如传感器网络中的时间）开始，然后将所有张量模式的一般图扩展到一般图。这种广泛适用的范式促使对经典问题和方法进行重新定义和改进，以创造性地应对基于张量的数据的挑战。我们综合了目前将GSP与张量分析相结合的努力而引起的共同主题，并突出了将GSP扩展到多路范式的未来方向。
Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures. As acquired data is increasingly taking the form of multi-way tensors, new signal processing tools are needed to maximally utilize the multi-way structure within the data. In this paper, we review modern signal processing frameworks generalizing GSP to multi-way data, starting from graph signals coupled to familiar regular axes such as time in sensor networks, and then extending to general graphs across all tensor modes. This widely applicable paradigm motivates reformulating and improving upon classical problems and approaches to creatively address the challenges in tensor-based data. We synthesize common themes arising from current efforts to combine GSP with tensor analysis and highlight future directions in extending GSP to the multi-way paradigm.
