This paper includes supplementary material for the paper [A.N. Gorban, A.
Zinovyev, Principal manifolds and graphs in practice: from molecular biology to
dynamical systems, International Journal of Neural Systems 20 (3) (2010),
219-232. E-print: arXiv:1001.1122 [cs.NE]]. We present details of the analysis
of the nonlinear quality of life index for 162 countries. This index is based
on four indicators: GDP per capita, Life expectancy at birth, Infant mortality
rate, and Tuberculosis incidence.
We present several applications of non-linear data modeling, using principal
manifolds and principal graphs constructed using the metaphor of elasticity
(elastic principal graph approach). These approaches are generalizations of the
Kohonen's self-organizing maps, a class of artificial neural networks. On
several examples we show advantages of using non-linear objects for data
approximation in comparison to the linear ones. We propose four numerical
criteria for comparing linear and non-linear mappings of datasets into the
spaces of lower dimension.