Locally Linear Embedding. It has several advantages over Isomap, including
faster optimization when implemented to take advantage of sparse matrix
algorithms, and better results with many problems. LLE also begins by
finding a set of the nearest neighbors of each point. It then computes
a set of weights for each point that best describe the point as a linear
combination of its neighbors. Finally, it uses an eigenvector-based
optimization technique to find the low-dimensional embedding of points,
such that each point is still described with the same linear combination
of its neighbors. LLE tends to handle non-uniform sample densities poorly
because there is no fixed unit to prevent the weights from drifting as
various regions differ in sample densities.