Learning from Multiple Graphs using a Sigmoid Kernel

Ricatte, Thomas; Garriga, Gemma; Gilleron, Remi; Tommasi, Marc

Type de document :

Communication dans un congrès avec actes

Titre :

Learning from Multiple Graphs using a Sigmoid Kernel

Auteur(s) :

Ricatte, Thomas [Auteur]
Machine Learning in Information Networks [MAGNET]
Garriga, Gemma [Auteur]
Machine Learning in Information Networks [MAGNET]
Gilleron, Remi [Auteur]

Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Machine Learning in Information Networks [MAGNET]
Tommasi, Marc [Auteur]

Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Machine Learning in Information Networks [MAGNET]

Titre de la manifestation scientifique :

The 12th International Conference on Machine Learning and Applications (ICMLA'13)

Ville :

Miami

Pays :

Etats-Unis d'Amérique

Date de début de la manifestation scientifique :

2013-12-04

Date de publication :

2013-12-04

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

This paper studies the problem of learning from a set of input graphs, each of them representing a different relation over the same set of nodes. Our goal is to merge those input graphs by embedding them into an Euclidean ...
Lire la suite >This paper studies the problem of learning from a set of input graphs, each of them representing a different relation over the same set of nodes. Our goal is to merge those input graphs by embedding them into an Euclidean space related to the commute time distance in the original graphs. This is done with the help of a small number of labeled nodes. Our algorithm output a combined kernel that can be used for different graph learning tasks. We consider two combination methods: the (classical) linear combination and the sigmoid combination. We compare the combination methods on node classification tasks using different semi-supervised graph learning algorithms. We note that the sigmoid combination method exhibits very positive results.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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