The Geodesic Self-Organizing Map and its error analysis

Authors:
Yingxin Wu;Masahiro Takatsuka
Affiliations:
The University of Sydney, National ICT Australia;The University of Sydney, National ICT Australia
Venue:
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Year:
2005

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Abstract

The Self-Organizing Map (SOM) is one of the popular Artificial Neural Networks which is a useful in clustering and visualizing complex high dimensional data. Conventional SOMs are based on the two-dimensional (2D) grid structure, which usually results in less accurate representation of the data. Several SOMs using spherical data structures have been proposed to remove the "border effect". In this paper, we compared our proposed Geodesic SOM (GeoSOM) with the 2D hexagonal SOM by experiments. The result shows that the GeoSOM not only runs as fast as the conventional 2D SOM, but also represents the data more accurately within fewer training epochs.