On global self-organizing maps.

ESANN'96, Bruge 22-26.4.1996 (in print)

Wlodzislaw Duch and Antoine Naud 
Department of Computer Methods, 
Nicholas Copernicus University,
ul. Grudziadzka 5, 87-100 Torun, Poland.
e-mail: duch,naud@phys.uni.torun.pl

Abstract. 

Self-Organizing Feature-Mapping (SOFM) algorithm is frequently used for
visualization of high-dimensional (input) data in a lower-dimensional
(target) space. This algorithm is based on adaptation of parameters in
local neighborhoods and therefore does not lead to the best global
visualization of the input space data clusters. SOFM is compared here
with alternative methods of global visualization of multidimensional data,
such as the multidimensional scaling (MDS) and Sammon non-linear mapping,
methods based on minimization of error function measuring topographical
distortions. SOFM is inferior as a visualization method but facilitates
faster classification. A combination of global methods with SOFM should
be useful for visualization and