Locally Linear Embedding
An S4 Class implementing Locally Linear Embedding (LLE)
LLE approximates the points in the manifold by linear combination of its neighbors. These linear combinations are the same inside the manifold and in highdimensional space.
funA function that does the embedding and returns a dimRedResult object.
stdparsThe standard parameters for the function.
Dimensionality reduction methods are S4 Classes that either be used
directly, in which case they have to be initialized and a full
list with parameters has to be handed to the @fun()
slot, or the method name be passed to the embed function and
parameters can be given to the ..., in which case
missing parameters will be replaced by the ones in the
@stdpars.
LLE can take the following parameters:
the number of neighbors for the knn graph., defaults to 50.
the number of embedding dimensions, defaults to 2.
Wraps around lle, only
exposes the parameters k and m.
Roweis, S.T., Saul, L.K., 2000. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323-2326. doi:10.1126/science.290.5500.2323
Other dimensionality reduction methods: AutoEncoder-class,
DRR-class,
DiffusionMaps-class,
DrL-class, FastICA-class,
FruchtermanReingold-class,
HLLE-class, Isomap-class,
KamadaKawai-class, MDS-class,
NNMF-class, PCA-class,
PCA_L1-class, UMAP-class,
dimRedMethod-class,
dimRedMethodList, kPCA-class,
nMDS-class, tSNE-class
dat <- loadDataSet("3D S Curve", n = 500)
## directy use the S4 class:
lle <- LLE()
emb <- lle@fun(dat, lle@stdpars)
## using embed():
emb2 <- embed(dat, "LLE", knn = 45)
plot(emb, type = "2vars")
plot(emb2, type = "2vars")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.