RDRToolbox (version 1.18.0)

LLE: Locally Linear Embedding

Description

Computes the Locally Linear Embedding as introduced in 2000 by Roweis, Saul and Lawrence.

Usage

LLE(data, dim=2, k)

Arguments

data

N x D matrix (N samples, D features)

dim

dimension of the target space

k

number of neighbours

Value

It returns a N x dim matrix (N samples, dim features) with the reduced input data

Details

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.

References

Roweis, Sam T. and Saul, Lawrence K., "Nonlinear Dimensionality Reduction by Locally Linear Embedding",2000;

Examples

## 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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