A Bayesian Variational principle for dynamic Self Organizing Maps
arXiv:2208.11337v1h-index: 5
Originality Synthesis-oriented
AI Analysis
This work addresses incremental improvements in machine learning for dynamic data analysis, specifically for researchers in unsupervised learning and neural networks.
The authors tackled the problem of training Self-Organizing Maps with adaptive neighborhood radius using a variational Bayesian framework, resulting in a method validated in non-stationary and high-dimensional settings and compared to another adaptive approach.
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.