Contradiction resolution and its application to self-organizing maps

In this paper, we propose a new type of information-theoretic method called “contradiction resolution.” Neurons are supposed to be evaluated by two different ways. First, the neurons must evaluate themselves for themselves, namely, self-evaluation. On the other hand, neurons must be evaluated by their neighboring neurons, namely, outer-evaluation. In a society of neurons, contradiction between self and outer-evaluation must be reduced as much as possible. We apply the method to self-organizing maps. Our method modifies the cooperation of neurons in such a way that difference between self and outer-evaluation is reduced. We applied the method with self-organizing maps to the visualization of dollar-yen exchange rates. Our method could produce explicit class structure in which the dollar-yen rates were divided into three specific periods and an additional one with the highest and lowest peaks.