A Bayesian Variational principle for dynamic Self Organizing Maps

Anthony Fillion; Thibaut Kulak; François Blayo

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arXiv · 2208.11337

A Bayesian Variational principle for dynamic Self Organizing Maps

Anthony Fillion·Thibaut Kulak·François Blayo

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Abstract

We propose organisation conditions that yield a method for training SOM with adaptative neighborhood radius in a variational Bayesian framework. This method is validated on a non-stationary setting and compared in an high-dimensional setting with an other adaptative method.

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cs.LG cs.NE stat.ML

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Anthony Fillion, Thibaut Kulak, François Blayo. 2022-08-24. A Bayesian Variational principle for dynamic Self Organizing Maps. https://arxiv.org/abs/2208.11337

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