Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries [article]

Jay S. Stanley III, Eric C. Chi, Gal Mishne
2020 arXiv   pre-print
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
more » ... al 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.
arXiv:2007.00041v2 fatcat:i2e77o5njrhkpfoxtdswdkpibm