Multiple kernel self-organizing maps

Madalina Olteanu (1) , Nathalie Villa-Vialaneix (2) , Christine Cierco-Ayrolles (2)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
2 MIAT INRA - Unité de Mathématiques et Informatique Appliquées de Toulouse

In a number of real-life applications, the user is interested in analyzing several sources of information together: a graph combined with the additional information known on its nodes, numerical variables measured on individuals and factors describing these individuals... The combination of all sources of information can help him to understand the dataset in its whole better. The present article focuses on such an issue, by using self-organizing maps. The use a kernel version of the algorithm allows us to combine various types of information and automatically tune the data combination. This approach is illustrated on a simulated example.

Domaines: Machine Learning [stat.ML]

Madalina Olteanu, Nathalie Villa-Vialaneix, Christine Cierco-Ayrolles. Multiple kernel self-organizing maps. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2013, Bruges, Belgium. pp.83. ⟨hal-00817920⟩