dimRed (version 0.2.3)

HLLE-class: Hessian Locally Linear Embedding

Description

An S4 Class implementing Hessian Locally Linear Embedding (HLLE)

Slots

fun

A function that does the embedding and returns a dimRedResult object.

stdpars

The 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.

Details

HLLE uses local hessians to approximate the curvines and is an extension to non-convex subsets in lowdimensional space.

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, LLE-class, MDS-class, NNMF-class, PCA-class, PCA_L1-class, UMAP-class, dimRedMethod-class, dimRedMethodList, kPCA-class, nMDS-class, tSNE-class

Examples

# NOT RUN {
dat <- loadDataSet("3D S Curve", n = 300)

## directy use the S4 class:
hlle <- HLLE()
emb <- hlle@fun(dat, hlle@stdpars)

## using embed():
emb2 <- embed(dat, "HLLE", knn = 45)

plot(emb, type = "2vars")
plot(emb2, type = "2vars")

# }

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