dimRed (version 0.2.5)

KamadaKawai-class: Graph Embedding via the Kamada Kawai Algorithm

Description

An S4 Class implementing the Kamada Kawai Algorithm for graph embedding.

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

KamadaKawai can take the following parameters:

ndim

The number of dimensions, defaults to 2. Can only be 2 or 3

knn

Reduce the graph to keep only the neares neighbors. Defaults to 100.

d

The distance function to determine the weights of the graph edges. Defaults to euclidean distances.

Implementation

Wraps around layout_with_kk. The parameters maxiter, epsilon and kkconst are set to the default values and cannot be set, this may change in a future release. The DimRed Package adds an extra sparsity parameter by constructing a knn graph which also may improve visualization quality.

Details

Graph embedding algorithms se the data as a graph. Between the nodes of the graph exist attracting and repelling forces which can be modeled as electrical fields or springs connecting the nodes. The graph is then forced into a lower dimensional representation that tries to represent the forces betweent he nodes accurately by minimizing the total energy of the attracting and repelling forces.

References

Kamada, T., Kawai, S., 1989. An algorithm for drawing general undirected graphs. Information Processing Letters 31, 7-15. https://doi.org/10.1016/0020-0190(89)90102-6

See Also

Other dimensionality reduction methods: AutoEncoder-class, DRR-class, DiffusionMaps-class, DrL-class, FastICA-class, FruchtermanReingold-class, HLLE-class, Isomap-class, LLE-class, MDS-class, NNMF-class, PCA-class, PCA_L1-class, UMAP-class, dimRedMethod-class, dimRedMethodList(), kPCA-class, nMDS-class, tSNE-class

Examples

dat <- loadDataSet("Swiss Roll", n = 200)
emb <- embed(dat, "KamadaKawai")
plot(emb, type = "2vars")