Accumulative Information Enhancement In The Self-Organizing Maps And Its Application To The Analysis Of Mission Statements

This paper proposes a new information-theoretic method based on the information enhancement method to extract important input variables. The information enhancement method was developed to detect important components in neural systems. Previous methods have focused on the detection of only the most important components, and therefore have failed to fully incorporated the information contained in the components into learning processes. In addition, it has been observed that the information enhancement method cannot always extract input information from input patterns. Thus, in this paper a computational method is developed to accumulate information content in the process of information enhancement. The method was applied to an artificial data set and the analysis of mission statements. The results demonstrate that while we were able to explicitly extract the symmetric properties of the data from the artificial data set, only one main factor was able to be extracted from the mission statement, namely, “contribution to the society”. The companies with higher profits tend to have mission statements concerning the society. The results can be considered to be a first step toward the full clarification of the importance of mission statements in actual business activities.