Self-organizing by Information Maximization: Realizing Self-Organizing Maps by Information-Theoretic Competitive Learning

2006 ◽  
pp. 925-934
Author(s):  
Ryotaro Kamimura
Keyword(s):  
Competitive Learning ◽  
Self Organizing Maps ◽  
Information Theoretic ◽  
Information Maximization ◽  
Self Organizing

scholarly journals Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

2014 ◽  
Vol 2014 ◽  
pp. 1-24 ◽  
Author(s):  
Ryotaro Kamimura
Keyword(s):  
Cooperative Networks ◽  
Class Structure ◽  
Small Individual ◽  
Self Organizing Maps ◽  
Information Theoretic ◽  
New Type ◽  
Points Of View ◽  
Information Theoretic Method ◽  
Self Organizing

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps.

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