Description Usage Arguments Details Value Author(s) References Examples
Computes the Locally Linear Embedding as introduced in 2000 by Roweis, Saul and Lawrence.
1 |
data |
N x D matrix (N samples, D features) |
dim |
dimension of the target space |
k |
number of neighbours |
Locally Linear Embedding (LLE) preserves local properties of the data by
representing each sample in the data by a linear combination of
its k nearest neighbours with each neighbour weighted
independently. LLE finally chooses the low-dimensional
representation that best preserves the weights in the target
space.
This R version is based on the Matlab implementation by Sam Roweis.
It returns a N x dim matrix (N samples, dim features) with the reduced input data
Christoph Bartenhagen
Roweis, Sam T. and Saul, Lawrence K., "Nonlinear Dimensionality Reduction by Locally Linear Embedding",2000;
1 2 3 | ## two dimensional LLE embedding of a 1.000 dimensional dataset using k=5 neighbours
d = generateData(samples=20, genes=1000, diffgenes=100, blocksize=10)
d_low = LLE(data=d[[1]], dim=2, k=5)
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