dimRed (version 0.2.3)

PCA_L1-class: Principal Component Analysis with L1 error.

Description

S4 Class implementing PCA with L1 error.

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

PCA can take the following parameters:

ndim

The number of output dimensions.

center

logical, should the data be centered, defaults to TRUE.

scale.

logical, should the data be scaled, defaults to FALSE.

fun

character or function, the method to apply, see the pcaL1 package

…

other parameters for fun

Implementation

Wraps around the different methods is the pcaL1 package. Because PCA can be reduced to a simple rotation, forward and backward projection functions are supplied.

Details

PCA transforms the data so that the L2 reconstruction error is minimized or the variance of the projected data is maximized. This is sensitive to outliers, L1 PCA minimizes the L1 reconstruction error or maximizes the sum of the L1 norm of the projected observations.

References

Park, Y.W., Klabjan, D., 2016. Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis, in: Data Mining (ICDM), 2016 IEEE 16th International Conference On. IEEE, pp. 430-438.

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, UMAP-class, dimRedMethod-class, dimRedMethodList, kPCA-class, nMDS-class, tSNE-class

Examples

# NOT RUN {
if(requireNamespace("pcaL1", quietly = TRUE)) {
dat <- loadDataSet("Iris")

## using the S4 Class
pca_l1 <- PCA_L1()
emb <- pca_l1@fun(dat, pca_l1@stdpars)

## using embed()
emb2 <- embed(dat, "PCA_L1")

plot(emb, type = "2vars")
plot(emb@inverse(emb@data), type = "3vars")
}

# }

Run the code above in your browser using DataCamp Workspace