Variants of self-organizing maps
- 1 January 1989
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 517-522 vol.2
- https://doi.org/10.1109/ijcnn.1989.118292
Abstract
Self-organizing maps have a connection with traditional vector quantization. A characteristic which makes them resemble certain biological brain maps, however, is the spatial order of their responses which is formed in the learning process. Two innovations are discussed: dynamic weighting of the input signals at each input of each cell, which improves the ordering when very different input signals are used, and definition of neighborhoods in the learning algorithm by the minimum spanning tree, which provides a far better and faster approximation of prominently structured density functions. It is cautioned that if the maps are used for pattern recognition and decision processes, it is necessary to fine-tune the reference vectors such that they directly define the decision borders.Keywords
This publication has 3 references indexed in Scilit:
- Statistical pattern recognition with neural networks: benchmarking studiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1988
- Vector quantization in speech codingProceedings of the IEEE, 1985
- Self-organized formation of topologically correct feature mapsBiological Cybernetics, 1982