Article thumbnail

Faster Training in Nonlinear ICA using MISEP

By Luis B. Almeida


MISEP has been proposed as a generalization of the INFOMAX method in two directions: (1) handling of nonlinear mixtures, and (2) learning the nonlinearities to be used at the outputs, making the method suitable to the separation of components with a wide range of statistical distributions. In all implementations up to now, MISEP had used multilayer perceptrons (MLPs) to perform the nonlinear ICA operation. Use of MLPs sometimes leads to a relatively slow training. This has been attributed, at least in part, to the non-local character of the MLP's units. This paper investigates the possibility of using a network of radial basis function (RBF) units for performing the nonlinear ICA operation. It shows that the local character of the RBF network's units allows a significant speedup in the training of the system. The paper gives a brief introduction to the basics of the MISEP method, and presents experimental results showing the speed advantage of using an RBF-based network to perform the ICA operation

Topics: Statistical Models, Machine Learning, Neural Nets
Year: 2002
OAI identifier:

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.

Suggested articles


  1. (1996). An objective function for independence,” in
  2. (1992). Blind separation of sources: A nonlinear neural algorithm,”
  3. (2000). Linear and nonlinear ICA based on mutual information,” in
  4. (1995). Nonlinear higher-order statistical decorrelation by volume-conserving neural architectures,”
  5. (2000). Nonlinear independent component analysis using ensemble learning: Theory,” in
  6. (1999). Separation of nonlinear mixtures using pattern repulsion,” in