In this example toy data is being processed using the Isomap algorithm
as described in

Silva, V. D., & Tenenbaum, J. B. (2003).
Global versus local methods in nonlinear dimensionality reduction.
Advances in Neural Information Processing Systems 15, 15(Figure 2), 721-728. MIT Press.
Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.9.3407&rep=rep1&type=pdf

Before applying to the data the landmark approximation is enabled with
specified number of landmarks. The landmark approximation is described in

Sparse multidimensional scaling using landmark points
V De Silva, J B Tenenbaum (2004) Technology, p. 1-4

After enabling the landmark approximation k parameter -- the number
of neighbors in the k nearest neighbor graph -- is initialized.
