Self Organizing Star (SOS) for health monitoring

Etienne Côme (1) , Marie Cottrell (1) , Michel Verleysen (1, 2) , Jérôme Lacaille (3)

1 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)
2 DICE - MachineLearning Group - DICE
3 SNECMA Villaroche [Moissy-Cramayel]

Self Organizing Maps (SOM) have been successfully applied in a lot of real world hard problems since their apparition. In this paper we present new topologies for SOM based on a planar graph. The design of a specific graph to encode prior information on the dataset topology is the central question addressed in this paper. In this context, star-shaped graphs are advocated for health monitoring applications, leading to a new kind of SOM that we denote by Self Organizing Star (SOS). Experiments using aircraft engine measurements show that SOS lead to meaningful and natural dataset representation.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Statistiques [math.ST]; Théorie [stat.TH]

Etienne Côme, Marie Cottrell, Michel Verleysen, Jérôme Lacaille. Self Organizing Star (SOS) for health monitoring. European conference on artificial neural networks,, Apr 2010, Bruges, Belgium. pp.99-104. ⟨hal-00446594⟩