  We present a new manifold learning algorithm that takes a set of data points
lying on or near a lower dimensional manifold as input, possibly with noise,
and outputs a simplicial complex that fits the data and the manifold. We have
implemented the algorithm in the case where the input data can be triangulated.
We provide triangulations of data sets that fall on the surface of a torus,
sphere, swiss roll, and creased sheet embedded in a fifty dimensional space. We
also discuss the theoretical justification of our algorithm.

Numerical Analysis