title: Functional network topology learning and sensitivity analysis based on ANOVA decomposition.
abstract: A new methodology for learning the topology of a functional network from data, based on the ANOVA decomposition technique, is presented. The method determines sensitivity (importance) indices that allow a decision to be made as to which set of interactions among variables is relevant and which is irrelevant to the problem under study. This immediately suggests the network topology to be used in a given problem. Moreover, local sensitivities to small changes in the data can be easily calculated. In this way, the dual optimization problem gives the local sensitivities. The methods are illustrated by their application to artificial and real examples.
authors.affiliation: Department of Applied Mathematics and Computational Sciences, University of Cantabria and University of Castilla-La Mancha, Spain. castie@unican.es
authors.forename: Enrique
authors.initials: E
authors.lastname: Castillo
authors.forename: Noelia
authors.initials: N
authors.lastname: Sánchez-Maroño
authors.forename: Amparo
authors.initials: A
authors.lastname: Alonso-Betanzos
authors.forename: Carmen
authors.initials: C
authors.lastname: Castillo
mesh_terms: D000704:Analysis of Variance; D001185:Artificial Intelligence; D003658:Decision Making, Computer-Assisted; D006801:Humans; D016571:Neural Networks, Computer