LLE: Locally Linear Embedding

In RDRToolbox: A package for nonlinear dimension reduction with Isomap and LLE.

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

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.

Value

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

Author(s)

Christoph Bartenhagen

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)

Example output

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 
Computing distance matrix ... done
Computing low dimensional emmbedding (using 5 nearest neighbours)... done

RDRToolbox documentation built on Nov. 8, 2020, 11:10 p.m.