lle

Nonlinear dimensionality reduction using locally linear embedding.

Input Arguments:

• Data matrix X, which is p by n. NOTE that this is the transpose of our usual data matrix.
• k is the number of neighbors.
• dmax is the maximum embedding dimensionality (i.e., number of dimensions in the lower-dimensional space).

Output Arguments:

• Y = embedding as dmax x N matrix

Synopsis

[Y] = lle(X,K,dmax)

References:

Roweis, S. T. and L. K. Saul. 2000. “Nonlinear dimensionality reduction by locally linear embedding,” Science, 290:2323-2326.