Publications associées à Learning of Structured Graph Dictionaries

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Graph Transformations preserving the stability number

Dominique de Werra

We analyze the relations between several graph transformations that were introduced to be used in procedures determining the stability number of a graph. We show that all these transformations can be decomposed into a sequence of edge deletions and twin de ...

Elsevier Science Bv2012

Lower bounds on the obstacle number of graphs

János Pach, Doemoetoer Palvoelgyi, Padmini Mukkamala

Given a graph G, an obstacle representation of G is a set of points in the plane representing the vertices of G, together with a set of connected obstacles such that two vertices of G are joined by an edge if and only if the corresponding points can be con ...

2012

Robust Non-Rigid Registration of 2D and 3D Graphs

Pascal Fua, Jan Kybic, Przemyslaw Rafal Glowacki

We present a new approach to matching graphs embedded in R2 or R3. Unlike earlier methods, our approach does not rely on the similarity of local appearance features, does not require an initial alignment, can handle partial matches, and can cope with non-l ...

2012

Tangled Thrackles

János Pach

A tangle is a graph drawn in the plane so that any pair of edges have precisely one point in common, and this point is either an endpoint or a point of tangency. If we allow a third option: the common point may be a proper crossing between the two edges, t ...

Springer Berlin Heidelberg2012

Beyond Graphs: A New Synthesis

Dario Floreano, Claudio Mattiussi, Daniel Marbach, Peter Dürr

Artiﬁcial neural networks, electronic circuits, and gene networks are some examples of systems that can be modeled as networks, that is, as collections of interconnected nodes. In this paper we introduce the concept of terminal graph (t-graph for short), w ...

2011

Dictionary learning in stereo imaging

Pascal Frossard, Ivana Tosic

This paper presents a new method for learning overcomplete dictionaries adapted to efficient joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms o ...

Institute of Electrical and Electronics Engineers2011

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Benjamin Hamner

Sparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled w ...

2011

Fast orthogonal sparse approximation algorithms over local dictionaries

Pierre Vandergheynst, Rémi Gribonval

In this work we present a new greedy algorithm for sparse approximation called LocOMP. LocOMP is meant to be run on local dictionaries made of atoms with much shorter supports than the signal length. This notably encompasses shift-invariant dictionaries an ...

2011

Learning of Structured Graph Dictionaries

Graph Chatbot

Chattez avec Graph Search

Graph Transformations preserving the stability number

Lower bounds on the obstacle number of graphs

Robust Non-Rigid Registration of 2D and 3D Graphs

Tangled Thrackles

Beyond Graphs: A New Synthesis

Parallelizing Machine Learning- Functionally: A Framework and Abstractions for Parallel Graph Processing

Dictionary learning in stereo imaging

Greedy Dictionary Selection for Sparse Representation

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

Fast orthogonal sparse approximation algorithms over local dictionaries

Lower bounds on the obstacle number of graphs

Robust Non-Rigid Registration of 2D and 3D Graphs

Tangled Thrackles

Fast orthogonal sparse approximation algorithms over local dictionaries

Parallelizing Machine Learning- Functionally: A Framework and Abstractions for Parallel Graph Processing

Graph Transformations preserving the stability number

Greedy Dictionary Selection for Sparse Representation

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

Beyond Graphs: A New Synthesis

Dictionary learning in stereo imaging