Large-scale data exploration with the hierarchically growing hyperbolic SOM.

Jörg Ontrup; Helge Ritter

doi:10.1016/j.neunet.2006.05.015

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Large-scale data exploration with the hierarchically growing hyperbolic SOM.

Jörg Ontrup·Helge Ritter

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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.

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Algorithms Animals Artificial Intelligence Cluster Analysis Computing Methodologies Humans Information Storage and Retrieval Models, Neurological Neural Networks, Computer Pattern Recognition, Automated Time Factors Journal Article Research Support, Non-U.S. Gov't

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Jörg Ontrup, Helge Ritter. 2006-06-27. Large-scale data exploration with the hierarchically growing hyperbolic SOM.. https://doi.org/10.1016/j.neunet.2006.05.015

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