1 research outputs found
Visualization of some multi-class erosion data using GDA and supervised SOM
We present our experience in visualization multivariate data when the
data vectors have class assignment. The goal is then to visualize the
data in such a way that data vectors belonging to different classes
(subgroups) appear differentiated as much as possible. We consider for
this purpose the traditional CDA (Canonical Discriminant Functions), the
GDA (Generalized Discriminant Analysis, Baudat and Anouar, 2000) and the
Supervised SOM (Kohonen, Makivasara, Saramaki 1984). The methods are
applied to a set of 3-dimensional erosion data containing N=3420 data
vectors subdivided into 5 classes of erosion risk. By performing the
mapping of these data to a plane, we hope to gain some experience how
the mentioned methods work in practice and what kind of visualization is
obtained. The final conclusion is that the traditional CDA is the best
both in speed (time) of the calculations and in the ability of
generalization