DRR-class {dimRed} | R Documentation

## Dimensionality Reduction via Regression

### Description

An S4 Class implementing Dimensionality Reduction via Regression (DRR).

### Details

DRR is a non-linear extension of PCA that uses Kernel Ridge regression.

### 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

DRR can take the following parameters:

ndim

  The number of dimensions

lambda

  The regularization parameter for the ridge regression.

kernel

  The kernel to use for KRR, defaults to `"rbfdot"`.

kernel.pars

  A list with kernel parameters, elements depend on the kernel used, `"rbfdot"` uses `"sigma"`.

pca

  logical, should an initial pca step be performed, defaults to `TRUE`.

pca.center

  logical, should the data be centered before the pca step. Defaults to `TRUE`.

pca.scale

  logical, should the data be scaled before the pca ste. Defaults to `FALSE`.

fastcv

  logical, should `fastCV` from the CVST package be used instead of normal cross-validation.

fastcv.test

  If `fastcv = TRUE`, separate test data set for fastcv.

cv.folds

  if `fastcv = FALSE`, specifies the number of folds for crossvalidation.

fastkrr.nblocks

  integer, higher values sacrifice numerical accuracy for speed and less memory, see below for details.

verbose

  logical, should the cross-validation results be printed out.

### Implementation

Wraps around `drr`, see there for details. DRR is a non-linear extension of principal components analysis using Kernel Ridge Regression (KRR, details see `constructKRRLearner` and `constructFastKRRLearner`). Non-linear regression is used to explain more variance than PCA. DRR provides an out-of-sample extension and a backward projection.

The most expensive computations are matrix inversions therefore the implementation profits a lot from a multithreaded BLAS library. The best parameters for each KRR are determined by cross-validaton over all parameter combinations of `lambda` and `kernel.pars`, using less parameter values will speed up computation time. Calculation of KRR can be accelerated by increasing `fastkrr.nblocks`, it should be smaller than n^1/3 up to sacrificing some accuracy, for details see `constructFastKRRLearner`. Another way to speed up is to use `pars$fastcv = TRUE` which might provide a more efficient way to search the parameter space but may also miss the global maximum, I have not ran tests on the accuracy of this method.

### References

Laparra, V., Malo, J., Camps-Valls, G., 2015. Dimensionality Reduction via Regression in Hyperspectral Imagery. IEEE Journal of Selected Topics in Signal Processing 9, 1026-1036. doi:10.1109/JSTSP.2015.2417833

### See Also

Other dimensionality reduction methods: `AutoEncoder-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`, `kPCA-class`, `nMDS-class`, `tSNE-class`

### Examples

    ## Not run:
    dat <- loadDataSet("variable Noise Helix", n = 200)[sample(200)]

    ## use the S4 Class directly:
    drr <- DRR()
    pars <- drr@stdpars
    pars$ndim <- 3
    emb <- drr@fun(dat, pars)

    ## simpler, use embed():
    emb2 <- embed(dat, "DRR", ndim = 3)

    plot(dat, type = "3vars")
    plot(emb, type = "3vars")
    plot(emb@inverse(emb@data@data[, 1, drop = FALSE]), type = "3vars")

    ## End(Not run)

[Package dimRed version 0.2.2 Index]