Abstract
The PCASOM is a novel self-organizing neural model that performs Principal Components Analysis (PCA). It is also related to the ASSOM network, but its training equations are simpler. The PCASOM has the ability to learn self-organizing maps of the means and correlations of complex input distributions. Here we propose a method to extend this capability to build intrinsic dimensionality maps. These maps model the underlaying structure of the input. Experimental results are reported, which show the self-organizing map formation performed by the proposed network.
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López-Rubio, E., Ortiz-de-Lazcano-Lobato, J.M., del Carmen Vargas-González, M., López-Rubio, J.M. (2005). Intrinsic Dimensionality Maps with the PCASOM. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_92
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DOI: https://doi.org/10.1007/11494669_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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