Title :
Visual stability improvement of SOM´s feature map by initial value assignment
Author :
Shinji, Momoi ; Tsutomu, Miyoshi
Author_Institution :
Dept. of Media Inf., Ryukoku Univ., Japan
Abstract :
In SOM learning, learning result depends on initial value of feature map and the location of the node or the distance between nodes on feature map is important factor to determine feature of individual data. For example, in data detection of hematopoietic tumors, given data is detected by location of the feature map. In this paper, we focused 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 :
computational complexity; generalisation (artificial intelligence); learning (artificial intelligence); self-organising feature maps; stability; SOM feature map; SOM learning; computational complexity; data detection; feature map location; generalization ability; hematopoietic tumors; individual data feature; initial value assignment; visual stability improvement; Computational complexity; Feature extraction; Informatics; Sorting; Stability criteria; Visualization; feature maps; improvement method; self-organizing map; visual stability;
Conference_Titel :
Fuzzy Systems (FUZZ), 2011 IEEE International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-7315-1
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2011.6007660