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Gasper: GrAph Signal ProcEssing in R
We present a short tutorial on to the use of the R gasper package. Gaspe...
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Graph signal processing for machine learning: A review and new perspectives
The effective representation, processing, analysis, and visualization of...
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A Tutorial on Graph Theory for Brain Signal Analysis
This tutorial paper refers to the use of graph-theoretic concepts for an...
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Towards stationary time-vertex signal processing
Graph-based methods for signal processing have shown promise for the ana...
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Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
This tutorial paper presents a didactic treatment of the emerging topic ...
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Sparse Sampling for Inverse Problems with Tensors
We consider the problem of designing sparse sampling strategies for mult...
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Multi Layer Analysis
This thesis presents a new methodology to analyze one-dimensional signal...
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Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries
Graph signal processing (GSP) is an important methodology for studying arbitrarily structured data. 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. 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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