1/03/2012 - 14:00 Thomas BURGER (CNRS, Unité Biologie à Grande Échelle (BGE) / Étude de la Dynamique des Protéomes (EDyP) (CEA /DSV / iRTSV)) Salle 1 - Tour IRMA
PerTurbo, an original, non-parametric and efficient classification method is presented here. In our framework, the manifold of each class is characterized by its Laplace-Beltrami operator, which is evaluated with classical methods involving the graph Laplacian. The classification criterion is established thanks to a measure of the magnitude of the spectrum perturbation of this operator. The first experiments show good performances against classical algorithms of the state-of-the-art. Moreover, from this measure is derived an efficient policy to design sampling queries in a context of active learning. Performances collected over toy examples and real world datasets assess the qualities of this strategy.