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

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A graph learning approach for light field image compression

Touradj Ebrahimi, Pascal Frossard, Irene Viola, Hermina Petric Maretic

In recent years, light field imaging has attracted the attention of the academic and industrial communities thanks to its enhanced rendering capabilities that allow to visualise contents in a more immersive and interactive way. However, those enhanced capa ...

Spie-Int Soc Optical Engineering2018

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

How to learn a graph from smooth signals

Vasilis Kalofolias

We propose a framework that learns the graph structure underlying a set of smooth signals. Given a real m by n matrix X whose rows reside on the vertices of an unknown graph, we learn the edge weights w under the smoothness assumption that trace(X^TLX) is ...

2016

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

Scaling a Reliable Distributed System

Alexandre David Olivier Maurer

We consider the problem of reliably connecting an arbitrarily large set of computers (nodes) with communication channels. Reliability means here the ability, for any two nodes, to remain connected (i.e., their ability to communicate) with probability at le ...

LPD EPFL2016

Downsampling of Signals on Graphs Via Maximum Spanning Trees

Quy Ha Nguyen, Minh Do

Downsampling of signals living on a general weighted graph is not as trivial as of regular signals where we can simply keep every other samples. In this paper we propose a simple, yet effective downsampling scheme in which the underlying graph is approxima ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Moment Semantics for Reversible Rule-Based Systems

Sandro Stucki

We develop a notion of stochastic rewriting over marked graphs – i.e. directed multigraphs with degree constraints. The approach is based on double-pushout (DPO) graph rewriting. Marked graphs are expressive enough to internalize the ‘no-dangling-edge’ con ...

Springer International Publishing2015

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

Relationships between entities in datasets are often of multiple nature, like geographical distance, social relationships, or common interests among people in a social network, for example. This information can naturally be modeled by a set of weighted and ...

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

Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks

Michael Christoph Gastpar, Naveen Goela

A model called the linear transform network (LTN) is proposed to analyze the compression and estimation of correlated signals transmitted over directed acyclic graphs (DAGs). An LTN is a DAG network with multiple source and receiver nodes. Source nodes tra ...

Institute of Electrical and Electronics Engineers2012