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Global and Local Uncertainty Principles for Signals on Graphs

Publications associées (33)

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Graph Laplacians for Rotation Equivariant Convolutional Neural Networks

Martino Milani

A fundamental problem in signal processing is to design computationally efficient algorithms to filter signals. In many applications, the signals to filter lie on a sphere. Meaningful examples of data of this kind are weather data on the Earth, or images o ...

2019

Testing Graph Clusterability: Algorithms and Lower Bounds

Mikhail Kapralov, Aidasadat Mousavifar, Ashish Hari Chiplunkar

We consider the problem of testing graph cluster structure: given access to a graph G = (V, E), can we quickly determine whether the graph can be partitioned into a few clusters with good inner conductance, or is far from any such graph? This is a generali ...

IEEE COMPUTER SOC2018

The invention relates to a method and an apparatus for encoding and/or decoding digital images, wherein said encoding apparatus (1100) comprises processing means (1110) configured for determining weights of a graph related to an image by minimizing a cost ...

2018

A Time-Vertex Signal Processing Framework

Nathanaël Perraudin, Benjamin Ricaud, Andreas Loukas, Francesco Grassi

An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate th ...

Institute of Electrical and Electronics Engineers2018

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

Everything You Always Wanted to Know about Multicore Graph Processing but Were Afraid to Ask

Willy Zwaenepoel, Baptiste Joseph Eustache Lepers, Jasmina Malicevic

Graph processing systems are used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. We perform a systematic comparison of various techniques proposed to sp ...

USENIX Association2017

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

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Nathanaël Perraudin, Benjamin Ricaud, Francesco Grassi

Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolv ...

2016

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

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