Progressive Concept Formation in Self-organising Maps
We review a technique for creating Self-organising Maps (SOMs) in a Feature space which is nonlinearly related to the original data space. We show that convergence is remarkably fast for this method. The resulting map has two properties which are interesting from a biological perspective: first, the learning forms topology preserving mappings extremely quickly; second, the learning is most refined for those parts of the feature space which is learned first and which have most data. By considering the linear feature space, we show that it is the interaction between the overcomplete basis in which learning takes place and the mixture of one-shot and incremental learning which comprises the method that gives the method its power. Finally, as an engineering application, we show that maps representing time series data are able to successfully extract the time-dependent structure in the series.
KeywordsFeature Space Incremental Learning Winning Neuron Original Data Space Early Sensory Processing
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- 1.E. Corchado and C. Fyfe. Initialising self-organising maps. In Fourth International Conference on Intelligent Data Engineering and Automated Learning, IDEAL2003, 2003. (submitted).Google Scholar
- 2.E. Corchado and C. Fyfe. Relevance and kernel self-organising maps. In International Conference on Artificial Neural Networks, ICANN2003, 2003. (submitted).Google Scholar
- 3.Hubel D. H. and Wiesel T. N. Receptive fields, binocular interaction and functional architecture in the cats visual cortex,. ournal of Physiology (London), 160:106–154, 1962.Google Scholar
- 4.Y. Han and C. Fyfe. Finding underlying factors in time series. Cybernetics and Systems: An International Journal, 33:297–323, March 2002.Google Scholar
- 5.Tuevo Kohonen. Self-Organising Maps. Springer, 1995.Google Scholar
- 6.D. MacDonald and C. Fyfe. The kernel self-organising map. In R.J. Howlett and L. C. Jain, editors, Fourth International Conference on Knowledge-based Intelligent Engineering Systems and Allied Technologies, KES2000, 2000.Google Scholar