kPCA-class {dimRed} | R Documentation

## Kernel PCA

### Description

An S4 Class implementing Kernel PCA

### Details

Kernel PCA is a nonlinear extension of PCA using kernel methods.

### 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

Kernel PCA can take the following parameters:

ndim

  the number of output dimensions, defaults to 2

kernel

  The kernel function, either as a function or a character vector with the name of the kernel. Defaults to `"rbfdot"`

kpar

  A list with the parameters for the kernel function, defaults to `list(sigma = 0.1)`

The most comprehensive collection of kernel functions can be found in `kpca`. In case the function does not take any parameters `kpar` has to be an empty list.

### Implementation

Wraps around `kpca`, but provides additionally forward and backward projections.

### References

Sch\"olkopf, B., Smola, A., M\"uller, K.-R., 1998. Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Computation 10, 1299-1319. https://doi.org/10.1162/089976698300017467

### See Also

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

### Examples

    ## Not run:
    dat <- loadDataSet("3D S Curve")

    ## use the S4 class directly:
    kpca <- kPCA()
    emb <- kpca@fun(dat, kpca@stdpars)

    ## simpler, use embed():
    emb2 <- embed(dat, "kPCA")

    plot(emb, type = "2vars")

    ## End(Not run)

[Package dimRed version 0.2.2 Index]