Improvement of visual stability by adjustment of feature maps and leaning data of SOM
MOMOI Shinji, MIYOSHI Tsutomu

Abstract
Based on the SOM learning algorithm, SOM learning is influenced by the sequence of learning data and the initial feature map. 
The location of the node or the distance between nodes on feature map is important factor to determine feature of individual data. 
In conventional method, initial value of feature map has set at random, so a different mapping appears even by same input data, 
so different impressions could be increased to the same data in different diagnosis. 
In this paper, we forcused on visual stability of SOM feature map, 
and we proposed new initialization method of SOM feature map. 
The purposes of proposed method are improvement of visual stability of SOM feature map, 
and utilization of generalization ability of SOM. 
By experiments, proposed method is visually stable than conventional method in the point of feature map location, 
and the computational complexity of proposed method is greatly reduced.

Keywords-component; self-organizing map; feature maps; visual stability; improvement method;