Learn a Nonlinear Manifold on Numeric Data
DimensionReduction learns a parametrized manifold on which most of the data lies. In this example, learn and visualize the manifold of three-dimensional data.
Generate a "Swiss roll" dataset.
Train a nonlinear dimension reducer on the dataset. The dimension is reduced from 3 to 2.
The inverse reduction 
can be interpreted as a parametric surface. Visualize this surface by generating (u, v) points and transforming them with the inverse reduction 
.
Now visualize the dataset in the original space, with each point colored according to its reduced variable u.
