A novel information geometric approach to variable selection in MLP networks

  • Eleuteri A
  • Tagliaferri R
  • Milano L
  • 16


    Mendeley users who have this article in their library.
  • 9


    Citations of this article.


In this paper, a novel information geometric-based variable selection criterion for multi-layer perceptron networks is described. It is based on projections of the Riemannian manifold defined by a multi-layer perceptron network on submanifolds defined by multi-layer perceptron networks with reduced input dimension. We show how the divergence between models can be used as a criterion for an efficient search in the space of networks with different inputs. Then, we show how the posterior probabilities of the models can be evaluated to rank the projected models. Finally, we test our algorithm on synthetic and real data, and compare its performances with other methods reported in literature. © 2005 Elsevier Ltd. All rights reserved.

Author-supplied keywords

  • Bayesian inference
  • Information geometry
  • Neural networks
  • Variable selection

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free