Improvement of SOM visual stability by adjusting feature maps and sorting of leaning data

• Published in: The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems pp. 488 - 493
• Main Authors: Momoi, Shinji, Miyoshi, Tsutomu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2012
• Subjects: feature maps; improvement method; self-organizing map; visual stability
• DOI: 10.1109/SCIS-ISIS.2012.6505368
• ISBN: 9781467327428; 1467327425

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 focused on visual stability of SOM feature map, and we proposed two 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 some experiments with both artificial data and benchmark data, two proposed methods are visually stable than conventional method in the point of feature map location, and the computational complexity of proposed method is greatly reduced.