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Semi-Supervised Learning with Spectral Graph Wavelets

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Generalized Total Variation Denoising Via Augmented Lagrangian Cycle Spinning With Haar Wavelets

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We consider the denoising of signals and images using regularized least-squares method. In particular, we propose a simple minimization algorithm for regularizers that are functions of the discrete gradient. By exploiting the connection of the discrete gra ...

Ieee2012

Generalized Total Variation Denoising via Augmented Lagrangian Cycle Spinning with Haar Wavelets

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

IEEE2012

A Windowed Graph Fourier Transform

Pierre Vandergheynst, Benjamin Ricaud, David Shuman

The prevalence of signals on weighted graphs is increasing; however, because of the irregular structure of weighted graphs, classical signal processing techniques cannot be directly applied to signals on graphs. In this paper, we define generalized transla ...

Ieee2012

A Windowed Graph Fourier Transform

Pierre Vandergheynst, Benjamin Ricaud, David Shuman

IEEE2012

Space crossing numbers

We define the crossing number for an embedding of a graph G into R^3, and prove a lower bound on it which almost implies the classical crossing lemma. We also give sharp bounds on the space crossing numbers of pseudo-random graphs. ...

2011

Information Processing and Structure of Dynamical Networks

Ali Ajdari Rad

Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...

EPFL2011

Chebyshev polynomial approximation for transductive learning on graphs

Nathanaël Perraudin

The goal of transductive learning is to find a way to recover the labels of lots of data with only a few known samples. In this work, we will work on graphs for two reasons. First, it’s possible to construct a graph from a given dataset with features. The ...

2011

Wavelets on graphs via spectral graph theory

Pierre Vandergheynst, Rémi Gribonval

We propose a novel method for constructing wavelet transforms of functions defined on the vertices of an arbitrary finite weighted graph. Our approach is based on defining scaling using the the graph analogue of the Fourier domain, namely the spectral deco ...

Elsevier2011

Wavelet Frames On Graphs Defined By Fmri Functional Connectivity

Dimitri Nestor Alice Van De Ville, Nora Leonardi

Multiscale representations such as the wavelet transform are useful for many signal processing tasks. Graphs are flexible models to represent complex networks and a spectral graph wavelet transform (SGWT) has recently been developed as a generalization of ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Wavelet transform on manifolds: old and new approaches

Pierre Vandergheynst

Given a two-dimensional smooth manifold M and a bijective pro jection p from M on a ﬁxed plane (or a subset of that plane), we explore systematically how a wavelet transform (WT) on M may be generated from a plane WT by the inverse projection. Examples whe ...

Elsevier2010