Abstract.
Spatio-temporal pattern recognition problems are particularly challenging. They typically involve detecting change that occurs over time in two-dimensional patterns. Analytic techniques devised for temporal data must take into account the spatial relationships among data points. An artificial neural network known as the self-organizing feature map (SOM) has been used to analyze spatial data. This paper further investigates the use of the SOM with spatio-temporal pattern recognition. The principles of the two-dimensional SOM are developed into a novel three-dimensional network and experiments demonstrate that (i) the three-dimensional network makes a better topological ordering and (ii) there is a difference in terms of the spatio-temporal analysis that can be made with the three-dimensional network.
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Received 21 October 1999 / Revised 11 February 2000 / Accepted 2 May 2000
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George, S. Spatio-Temporal Analysis with the Self-Organizing Feature Map. Knowledge and Information Systems 2, 359–372 (2000). https://doi.org/10.1007/PL00011647
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DOI: https://doi.org/10.1007/PL00011647