Isomap-class {dimRed}R Documentation

Isomap embedding


An S4 Class implementing the Isomap Algorithm


The Isomap algorithm approximates a manifold using geodesic distances on a k nearest neighbor graph. Then classical scaling is performed on the resulting distance matrix.



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


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.


Isomap can take the following parameters:


The number of nearest neighbors in the graph. Defaults to 50.


The number of embedding dimensions, defaults to 2.


Should the geodesic distance matrix be kept, if TRUE, access it as getOtherData(x)$geod


The dimRed package uses its own implementation of Isomap which also comes with an out of sample extension (known as landmark Isomap). The default Isomap algorithm scales computationally not very well, the implementation here uses nn2 for a faster search of the nearest neighbors. If data are too large it may be useful to fit a subsample of the data and use the out-of-sample extension for the other points.


Tenenbaum, J.B., Silva, V. de, Langford, J.C., 2000. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319-2323.

See Also

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


if(requireNamespace(c("RSpectra", "igraph", "RANN"), quietly = TRUE)) {

dat <- loadDataSet("3D S Curve", n = 500)
emb <- embed(dat, "Isomap", knn = 10)

## or simpler, use embed():
samp <- sample(nrow(dat), size = 200)
emb2 <- embed(dat[samp], "Isomap", .mute = NULL, knn = 10)
emb3 <- predict(emb2, dat[-samp])

plot(emb2, type = "2vars")
plot(emb3, type = "2vars")


[Package dimRed version 0.2.6 Index]