Concept

Matrice laplacienne

Publications associées (131)

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

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti, Robin Jonathan Demesmaeker

The study of complex systems greatly benefits from graph models and their analysis. In particular, the eigendecomposition of the graph Laplacian lets emerge properties of global organization from local interactions; e.g., the Fiedler vector has the smalles ...

Institute of Electrical and Electronics Engineers2017

Stationary signal processing on graphs

Pierre Vandergheynst, Nathanaël Perraudin

Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or network ...

Institute of Electrical and Electronics Engineers2017

Graph-based Methods for Visualization and Clustering

Johann Paratte

The amount of data that we produce and consume is larger than it has been at any point in the history of mankind, and it keeps growing exponentially. All this information, gathered in overwhelming volumes, often comes with two problematic characteristics: ...

EPFL2017

Graph-based structures in data science

Nathanaël Perraudin

State-of-the-art data analysis tools have to deal with high-dimensional data. Fortunately, the inherent dimensionality of data is often much smaller, as it has an internal structure limiting its degrees of freedom. In most cases, this structure can be appr ...

EPFL2017

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

Plug-and-play design of current controllers for grid-feeding converters in DC microgrids

Giancarlo Ferrari Trecate, Michele Tucci, Raffaele Soloperto, Renke Han

In this paper, we address the problem of synthesizing decentralized current controllers for grid-feeding converters of current-controlled distributed generation units (CDGUs) in dc microgrids (MGs). Notably, a plug-and-play (PnP) design procedure is propos ...

IEEE2017

Learning time varying graphs

Pascal Frossard, Andreas Loukas, Vasilis Kalofolias

We consider the problem of inferring the hidden structure of high-dimensional dynamic systems from the perspective of graph learning. In particular, we aim at capturing the dynamic relationships between nodes by a sequence of graphs. Our approach is motiva ...

Ieee2017

Denoising radio interferometric images by subspace clustering

Paul Hurley, Matthieu Martin Jean-André Simeoni, Nezihe Merve Gürel

Radio interferometry usually compensates for high levels of noise in sensor/antenna electronics by throwing data and energy at the problem: observe longer, then store and process it all. Furthermore, only the end image is cleaned, reducing flexibility subs ...

2017

Case-Control Discrimination Through Effective Brain Connectivity

Luca Dodero

Functional and structural connectivity convey different information about the brain. The integration of these different approaches is receiving growing attention from the research community, as it can shed new light on brain functions. This manuscript prop ...

Ieee2017

Learning the weight matrix for sparsity averaging in compressive imaging

Jean-Philippe Thiran, Dimitris Perdios, Adrien Georges Jean Besson

We propose to map the fast iterative shrinkage-thresholding algorithm to a deep neural network (DNN), with a sparsity prior in a concatenation of wavelet bases, in the context of compressive imaging. We exploit the DNN architecture to learn the optimal wei ...

2017

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