title: Use of multilayer feedforward neural nets as a display method for multidimensional distributions.
abstract: We present a new method based on multilayer feedforward neural nets for displaying an n-dimensional distribution in a projected space of 1, 2 or 3 dimensions. A fully nonlinear net with several hidden layers is used. Efficient learning is achieved using multi-seed backpropagation. As a principal component analysis (PCA), the proposed method is useful for extracting information on the structure of the data set, but unlike the PCA, the transformation between the original distribution and the projected one is not restricted to be linear. Artificial examples and a real application are presented in order to show the reliability and potential of the method.
authors.affiliation: Departament d'Estructura i Constituens de la Materia/IFAE, Universitat de Barcelona Diagonal 647, Spain. garrido@ecm.ub.es
authors.forename: L
authors.initials: L
authors.lastname: Garrido
authors.forename: V
authors.initials: V
authors.lastname: Gaitan
authors.forename: M
authors.initials: M
authors.lastname: Serra-Ricart
authors.forename: X
authors.initials: X
authors.lastname: Calbet
mesh_terms: D001185:Artificial Intelligence; D016000:Cluster Analysis; D003199:Computer Systems; D016571:Neural Networks, Computer; D013028:Space Perception