4.6k citations · 5.4k across the 15 of their papers we have counts for
17 papers
Graph Signal Processing: History, Development, Impact, and Outlook
Geert Leus, Antonio G. Marques, José M. F. Moura +2
Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are ve…
Graph Filters for Signal Processing and Machine Learning on Graphs
Elvin Isufi, Fernando Gama, David I. Shuman +1
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and mac…
Signal Processing on the Permutahedron: Tight Spectral Frames for Ranked Data Analysis
Yilin Chen, Jennifer DeJong, Tom Halverson +1
Ranked data sets, where m judges/voters specify a preference ranking of n objects/candidates, are increasingly prevalent in contexts such as political elections, computer vision, r…
Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison (Extended Cut)
David I Shuman
Representing data residing on a graph as a linear combination of building block signals can enable efficient and insightful visual or statistical analysis of the data, and such rep…
Spectrum-Adapted Polynomial Approximation for Matrix Functions
Li Fan, David I Shuman, Shashanka Ubaru +1
We propose and investigate two new methods to approximate for large, sparse, Hermitian matrices . The main idea behind both methods is to first estimat…
Scalable -Channel Critically Sampled Filter Banks for Graph Signals
Shuni Li, Yan Jin, David I Shuman
We investigate a scalable -channel critically sampled filter bank for graph signals, where each of the filters is supported on a different subband of the graph Laplacian spe…
Global and Local Uncertainty Principles for Signals on Graphs
Nathanael Perraudin, Benjamin Ricaud, David Shuman +1
Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluatin…
GSPBOX: A toolbox for signal processing on graphs
Nathanaël Perraudin, Johan Paratte, David Shuman +4
This document introduces the Graph Signal Processing Toolbox (GSPBox) a framework that can be used to tackle graph related problems with a signal processing approach. It explains t…
UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods
Nathanael Perraudin, Vassilis Kalofolias, David Shuman +1
Convex optimization is an essential tool for machine learning, as many of its problems can be formulated as minimization problems of specific objective functions. While there is a…
Learning parametric dictionaries for graph signals
Dorina Thanou, David I Shuman, Pascal Frossard
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast im…
Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames
David I Shuman, Christoph Wiesmeyr, Nicki Holighaus +1
We consider the problem of designing spectral graph filters for the construction of dictionaries of atoms that can be used to efficiently represent signals residing on weighted gra…
A Multiscale Pyramid Transform for Graph Signals
David I Shuman, Mohammad Javad Faraji, Pierre Vandergheynst
Multiscale transforms designed to process analog and discrete-time signals and images cannot be directly applied to analyze high-dimensional data residing on the vertices of a weig…
Vertex-Frequency Analysis on Graphs
David I Shuman, Benjamin Ricaud, Pierre Vandergheynst
One of the key challenges in the area of signal processing on graphs is to design dictionaries and transform methods to identify and exploit structure in signals on weighted graphs…
The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
David I Shuman, Sunil K. Narang, Pascal Frossard +2
In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field…
Distributed Signal Processing via Chebyshev Polynomial Approximation
David I Shuman, Pierre Vandergheynst, Daniel Kressner +1
Unions of graph multiplier operators are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the ap…
Chebyshev Polynomial Approximation for Distributed Signal Processing
David I Shuman, Pierre Vandergheynst, Pascal Frossard
Unions of graph Fourier multipliers are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the app…
Energy-Efficient Transmission Scheduling with Strict Underflow Constraints
David I Shuman, Mingyan Liu, Owen Q. Wu
We consider a single source transmitting data to one or more receivers/users over a shared wireless channel. Due to random fading, the wireless channel conditions vary with time an…