ArticleIEEE signal processing magazine2020
Multiway Graph Signal Processing on Tensors: Integrative analysis of irregular geometries.
Article in IEEE signal processing magazine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Graph Laplacian Learning with Exponential Family Noise.IEEE transactions on signal and information processing over networks · 2025Article
- Multi-scale affinities with missing data: Estimation and applications.Statistical analysis and data mining · 2022Article
- Smooth graph learning for functional connectivity estimation.NeuroImage · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
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.
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Registered trials
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