DocumentCode :
394151
Title :
Economic states on neuronic maps
Author :
Liou, Cheng-Yuan ; Kuo, Yen-Ting
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Volume :
2
fYear :
2002
fDate :
18-22 Nov. 2002
Firstpage :
787
Abstract :
We test the idea of visualizing economic statistics data on self-organization related maps, which are the LLE, ISOMAP and GTM maps. We report initial results of this work. These three maps all have distinguished theoretical foundations. The statistic data usually span high-dimensional space, sometimes more than 10 dimensions. To perceive these data as a whole and to foresee future trends, perspective visualization assistance is an important issue. We use economic statistics for the United States over the past 25 years (1977 to 2001) and apply them on the maps. The results from these three maps display historic events along with their trends and significance.
Keywords :
data visualisation; economics; self-organising feature maps; United States; economic states; generative topographic mapping; isometric feature mapping; locally linear embedding; neuronic maps; self-organization maps; statistic data; Automatic testing; Computer science; Cost function; Councils; Data visualization; Displays; Economic indicators; Statistical analysis; Statistics; Unemployment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
Type :
conf
DOI :
10.1109/ICONIP.2002.1198166
Filename :
1198166
Link To Document :
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