An enhanced self-organizing incremental neural network for online unsupervised learning

Authors:
Shen Furao;Tomotaka Ogura;Osamu Hasegawa
Affiliations:
The State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, PR China and Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Japan;Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Japan;Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Japan
Venue:
Neural Networks
Year:
2007

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Abstract

An enhanced self-organizing incremental neural network (ESOINN) is proposed to accomplish online unsupervised learning tasks. It improves the self-organizing incremental neural network (SOINN) [Shen, F., Hasegawa, O. (2006a). An incremental network for on-line unsupervised classification and topology learning. Neural Networks, 19, 90-106] in the following respects: (1) it adopts a single-layer network to take the place of the two-layer network structure of SOINN; (2) it separates clusters with high-density overlap; (3) it uses fewer parameters than SOINN; and (4) it is more stable than SOINN. The experiments for both the artificial dataset and the real-world dataset also show that ESOINN works better than SOINN.