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When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum

Publications associées (35)

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Graph learning under sparsity priors

Pascal Frossard, Hermina Petric Maretic

Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. ...

Ieee2017

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature ...

EPFL2016

A Spectral Method for Generating Surrogate Graph Signals

Dimitri Nestor Alice Van De Ville, Elvira Pirondini, Martina Coscia, Anna Vybornova

The increasing availability of network data is leading to a growing interest in processing of signals on graphs. One notable tool for extending conventional signal-processing operations to networks is the graph Fourier transform that can be obtained as the ...

Institute of Electrical and Electronics Engineers2016

Graph-Based Representation and Coding of Multiview Images

Pascal Frossard, Bénédicte Motz

Instead of lossily coding depth images resulting in undesirable geometric distortion, graph-based representation (GBR) describes disparity information as a graph with a controllable accuracy. In this paper, we propose a more compact graphical representatio ...

Ieee2016

From data to structures

Vasilis Kalofolias

Weighted undirected graphs are a simple, yet powerful way to encode structure in data. A first question we need to address regarding such graphs is how to use them effectively to enhance machine learning problems. A second but more important question is ho ...

EPFL2016

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

Matthieu Martin Jean-André Simeoni

Real-life acquisition systems are fundamentally limited in their ability to reproduce point sources. For example, a point source object, a star say, observed with an optical telescope is blurred by the imperfect lenses composing the system. Mathematically, ...

2016

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

Real-Time Intelligent Clustering for Graph Visualization

Lionel Jérémie Martin

We present a tool for the interactive exploration and analysis of large clustered graphs. The tool empowers users to control the granularity of the graph, either by direct interaction (collapsing/expanding clusters) or via a slider that automatically compu ...

SciTePress2013

Parametric dictionary learning for graph signals

Pascal Frossard, 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

Intelligent Clustering for Graph Visualization

Lionel Jérémie Martin

More and more areas use graphs for the representation of their data because it gives a connection-oriented perspective. Unfortunately, datasets are constantly growing in size, while devices have increasingly smaller screens (tablets, smartphones, etc). In ...

2013