Publications de David Shuman | EPFL Graph Search

Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison

A major line of work in graph signal processing [2] during the past 10 years has been to design new transform methods that account for the underlying graph structure to identify and exploit structure in data residing on a connected, weighted, undirected gr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Distributed Signal Processing via Chebyshev Polynomial Approximation

Pascal Frossard, David Shuman, Pierre Vandergheynst, Daniel Kressner

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 application of these operators. The proposed method features approximations of ...

2018

A Multiscale Pyramid Transform for Graph Signals

Pierre Vandergheynst, David Shuman, Mohammadjavad Faraji

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 weighted graph, as they do not capture the intrinsic topology of the graph data ...

Institute of Electrical and Electronics Engineers2016

Vertex-Frequency Analysis on Graphs

Pierre Vandergheynst, Benjamin Ricaud, David Shuman

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. To do so, we need to account for the intrinsic geometric structure of the ...

Academic Press Inc Elsevier Science2016

Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

Pierre Vandergheynst, David Shuman

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 graphs. While the filters used in previous spectral graph wavelet constructions ...

Institute of Electrical and Electronics Engineers2015

Learning Parametric Dictionaries for Signals on Graphs

Pascal Frossard, Ntorina Thanou, David Shuman

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 implementation of the dictionary. To sparsely represent signals residing on wei ...

Institute of Electrical and Electronics Engineers2014

On the Sparsity of Wavelet Coefficients for Signals on Graphs

Pierre Vandergheynst, Benjamin Ricaud, David Shuman

A number of new localized, multiscale transforms have recently been introduced to analyze data residing on weighted graphs. In signal processing tasks such as regularization and compression, much of the power of classical wavelets on the real line is deriv ...

Spie-Int Soc Optical Engineering2013

Parametric dictionary learning for graph signals

Pascal Frossard, Ntorina Thanou, David Shuman

We propose a parametric dictionary learning algorithm to design structured dictionaries that sparsely represent graph signals. We incorporate the graph structure by forcing the learned dictionaries to be concatenations of subdictionaries that are polynomia ...

2013

The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

Pascal Frossard, Pierre Vandergheynst, David Shuman

Ieee-Inst Electrical Electronics Engineers Inc2013

Chebyshev Polynomial Approximation for Distributed Signal Processing

Pascal Frossard, Pierre Vandergheynst, David Shuman

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 application of these operators to the high-dimensional signals collected by sen ...

2011