title: Growing hierarchical probabilistic self-organizing graphs.
abstract: Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.
authors.affiliation: Department of Computer Languages and Computer Science, University of Málaga, Málaga, Spain. ezeqlr@lcc.uma.es
authors.forename: Ezequiel
authors.initials: E
authors.lastname: López-Rubio
authors.forename: Esteban José
authors.initials: EJ
authors.lastname: Palomo
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D011336:Probability; D013269:Stochastic Processes