A hierarchically growing hyperbolic self-organizing map for rapid structuring of large data sets
Joerg Ontrup, Helge Joachim Ritter · PUB – Publications at Bielefeld University (Bielefeld University) · 2005
We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map (H2SOM) featuring two extensions of the HSOM (hyperbolic SOM): (i) a hierarchically growing variant that allows for incremental training with an automated adaptation of lattice size to achieve a prescribed quantization error and (ii) an approximate best match search that utilizes the special structure of the hyperbolic lattice to achieve a tremendous speed-up for large map sizes. Using the MNIST database as a benchmark dataset, we show that the H2SOM yields a highly efficient visualization algorithm that combines the virtues of the SOM with extremely rapid training and low quantization & classification errors.