Abstract
Self-organizing neural networks are usually focused on prototype learning, while the topology is held fixed during the learning process. Here we propose a method to adapt the topology of the network so that it reflects the internal structure of the input distribution. This leads to a self-organizing graph, where each unit is a mixture component of a Mixture of Gaussians (MoG). The corresponding update equations are derived from the stochastic approximation framework. Experimental results are presented to show the self-organization ability of our proposal and its performance when used with multivariate datasets.
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López-Rubio, E., Ortiz-de-Lazcano-Lobato, J.M., Vargas-González, M.C. (2009). Probabilistic Self-Organizing Graphs. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_23
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DOI: https://doi.org/10.1007/978-3-642-02478-8_23
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