Enhancing and Relaxing Competitive Units for Feature Discovery
Neural Processing Letters
A novel audio color watermarking scheme based on self-organizing map
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Self-enhancement learning: self-supervised and target-creating learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Effects of widely separated clusters on lotto-type competitive learning with particle swarm features
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Directly optimizing topology-preserving maps with evolutionary algorithms
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
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In this paper, we propose a new information-theoretic method to produce explicit self-organizing maps (SOMs). Competition is realized by maximizing mutual information between input patterns and competitive units. Competitive unit outputs are computed by the Gaussian function of distance between input patterns and competitive units. A property of this Gaussian function is that, as distance becomes smaller, a neuron tends to fire strongly. Cooperation processes are realized by taking into account the firing rates of neighboring neurons. We applied our method to uniform distribution learning, chemical compound classification and road classification. Experimental results confirmed that cooperation processes could significantly increase information content in input patterns. When cooperative operations are not effective in increasing information, mutual information as well as entropy maximization is used to increase information. Experimental results showed that entropy maximization could be used to increase information and to obtain clearer SOMs, because competitive units are forced to be equally used on average.