| HLLE-class {dimRed} | R Documentation |
Hessian Locally Linear Embedding
Description
An S4 Class implementing Hessian Locally Linear Embedding (HLLE)
Details
HLLE uses local hessians to approximate the curvines and is an extension to non-convex subsets in lowdimensional space.
Slots
funA function that does the embedding and returns a dimRedResult object.
stdparsThe standard parameters for the function.
General usage
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.
Parameters
HLLE can take the following parameters:
- knn
neighborhood size
- ndim
number of output dimensions
Implementation
Own implementation, sticks to the algorithm in Donoho and Grimes (2003). Makes use of sparsity to speed up final embedding.
References
Donoho, D.L., Grimes, C., 2003. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. PNAS 100, 5591-5596. doi:10.1073/pnas.1031596100
See Also
Other dimensionality reduction methods:
AutoEncoder-class,
DRR-class,
DiffusionMaps-class,
DrL-class,
FastICA-class,
FruchtermanReingold-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
Examples
if(requireNamespace(c("RSpectra", "Matrix", "RANN"), quietly = TRUE)) {
dat <- loadDataSet("3D S Curve", n = 300)
emb <- embed(dat, "HLLE", knn = 15)
plot(emb, type = "2vars")
}