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(Neural Computation. 2004;16:2459-2481.)
© 2004 The MIT Press

Letter

Principal Components Analysis Competitive Learning

Ezequiel López-Rubio

ezeqlr{at}lcc.uma.es, Department of Computer Science and Artificial Intelligence, University of Málaga, Campus de Teatinos, s/n. 29071 Málaga, Spain

Juan Miguel Ortiz-de-Lazcano-Lobato

jmortiz{at}lcc.uma.es, Department of Computer Science and Artificial Intelligence, University of Málaga, Campus de Teatinos, s/n. 29071 Málaga, Spain

José Muñoz-Pérez

munozp{at}lcc.uma.es, Department of Computer Science and Artificial Intelligence, University of Málaga, Campus de Teatinos, s/n. 29071 Málaga, Spain

José Antonio Gómez-Ruiz

janto{at}lcc.uma.es, Department of Computer Science and Artificial Intelligence, University of Málaga, Campus de Teatinos, s/n. 29071 Málaga, Spain

We present a new neural model that extends the classical competitive learning by performing a principal components analysis (PCA) at each neuron. This model represents an improvement with respect to known local PCA methods, because it is not needed to present the entire data set to the network on each computing step. This allows a fast execution while retaining the dimensionality-reduction properties of the PCA. Furthermore, every neuron is able to modify its behavior to adapt to the local dimensionality of the input distribution. Hence, our model has a dimensionality estimation capability. The experimental results we present show the dimensionality-reduction capabilities of the model with multisensor images.






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