Semi-supervised learning of dynamic self-organising maps

Authors:
Arthur Hsu;Saman K. Halgamuge
Affiliations:
Dynamic Systems and Control Group, Department of Mechanical and Manufacturing Engineering, University of Melbourne, Victoria, Australia;Dynamic Systems and Control Group, Department of Mechanical and Manufacturing Engineering, University of Melbourne, Victoria, Australia
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

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Abstract

We present a semi-supervised learning method for the Growing Self-Organising Maps (GSOM) that allows fast visualisation of data class structure on the 2D network. Instead of discarding data with missing values, the network can be trained from data with up to 60% of their class labels and 25% of attribute values missing, while able to make class prediction with over 90% accuracy for the benchmark datasets used.