title: Large-scale data exploration with the hierarchically growing hyperbolic SOM.
abstract: 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 and the Reuters-21578 database as benchmark datasets, 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 and classification errors.
authors.affiliation: Bielefeld University, Faculty of Technology, Neuroinformatics Group, PO Box 100131, 33501 Bielefeld, Germany. jontrup@techfak.uni-bielefeld.de
authors.forename: Jörg
authors.initials: J
authors.lastname: Ontrup
authors.forename: Helge
authors.initials: H
authors.lastname: Ritter
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003205:Computing Methodologies; D006801:Humans; D016247:Information Storage and Retrieval; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D013997:Time Factors