期刊
NEURAL NETWORKS
卷 19, 期 6-7, 页码 751-761出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2006.05.015
关键词
hyperbolic self-organizing maps; growing network; hierarchical clustering; text mining
We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map ((HSOM)-S-2) 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. (c) 2006 Elsevier Ltd. All rights reserved.
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