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