Abstract
A topology-selection method for self-organizing maps (SOMs) based on empirical Bayesian inference is presented. This method is a natural extension of the hyperparameter-selection method presented earlier, in which the SOM algorithm is regarded as an estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior on the centroid parameters, and optimal hyperparameters are obtained by maximizing their evidence. In the present paper, comparisons between models with different topologies are made possible by further specifying the prior of the centroid parameters with an additional hyperparameter. In addition, a fast hyperparameter-search algorithm using the derivatives of evidence is presented. The validity of the methods presented is confirmed by simulation experiments.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
