mlpack (version 4.5.1)

pca: Principal Components Analysis

Description

An implementation of several strategies for principal components analysis (PCA), a common preprocessing step. Given a dataset and a desired new dimensionality, this can reduce the dimensionality of the data using the linear transformation determined by PCA.

Usage

pca(
  input,
  decomposition_method = NA,
  new_dimensionality = NA,
  scale = FALSE,
  var_to_retain = NA,
  verbose = getOption("mlpack.verbose", FALSE)
)

Value

A list with several components:

output

Matrix to save modified dataset to (numeric matrix).

Arguments

input

Input dataset to perform PCA on (numeric matrix).

decomposition_method

Method used for the principal components analysis: 'exact', 'randomized', 'randomized-block-krylov', 'quic'. Default value "exact" (character).

new_dimensionality

Desired dimensionality of output dataset. If 0, no dimensionality reduction is performed. Default value "0" (integer).

scale

If set, the data will be scaled before running PCA, such that the variance of each feature is 1. Default value "FALSE" (logical).

var_to_retain

Amount of variance to retain; should be between 0 and 1. If 1, all variance is retained. Overrides -d. Default value "0" (numeric).

verbose

Display informational messages and the full list of parameters and timers at the end of execution. Default value "getOption("mlpack.verbose", FALSE)" (logical).

Author

mlpack developers

Details

This program performs principal components analysis on the given dataset using the exact, randomized, randomized block Krylov, or QUIC SVD method. It will transform the data onto its principal components, optionally performing dimensionality reduction by ignoring the principal components with the smallest eigenvalues.

Use the "input" parameter to specify the dataset to perform PCA on. A desired new dimensionality can be specified with the "new_dimensionality" parameter, or the desired variance to retain can be specified with the "var_to_retain" parameter. If desired, the dataset can be scaled before running PCA with the "scale" parameter.

Multiple different decomposition techniques can be used. The method to use can be specified with the "decomposition_method" parameter, and it may take the values 'exact', 'randomized', or 'quic'.

Examples

# For example, to reduce the dimensionality of the matrix "data" to 5
# dimensions using randomized SVD for the decomposition, storing the output
# matrix to "data_mod", the following command can be used:

if (FALSE) {
output <- pca(input=data, new_dimensionality=5,
  decomposition_method="randomized")
data_mod <- output$output
}