Large-scale data exploration with the hierarchically growing hyperbolic SOM

We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map (H2 SOM) 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 and the Reuters-21578 database as benchmark datasets, we show that the H2 SOM yields a highly efficient visualization algorithm that combines the virtues of the SOM with extremely rapid training and low quantization and classification errors. Key words: Hyperbolic Self-organizing maps, Growing network, Hierarchical Clustering, Text Mining

• Type: Journal
• Year: 2006
• Where: NN
• Authors: Jörg Ontrup, Helge Ritter
• Keywords: Hyperbolic Self-organizing Maps, Hyperbolic Som, Neural Networks, NN 2006, Prescribed Quantization Error