NAME

       mlpack_pca - principal components analysis

SYNOPSIS

        mlpack_pca [-h] [-v]

DESCRIPTION

       This  program performs principal components analysis on the given dataset using the exact,
       randomized 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.

REQUIRED INPUT OPTIONS

       --input_file (-i) [string]
              Input dataset to perform PCA on.

OPTIONAL INPUT OPTIONS

       --decomposition_method (-c) [string] Method used  for  the  principalcomponents  analysis:
       'exact', 'randomized', 'quic'.  Default value 'exact'.

       --help (-h)
              Default help info.

       --info [string]
              Get  help  on a specific module or option.  Default value ''.  --new_dimensionality
              (-d) [int] Desired dimensionality  of  output  dataset.  If  0,  no  dimensionality
              reduction is performed.  Default value 0.

       --scale (-s)
              If  set, the data will be scaled before running PCA, such that the variance of each
              feature is

              1.

                  --var_to_retain (-r) [double] Amount of variance to retain; should be between 0
                  and 1. If 1, all variance is retained.  Overrides -d. Default value 0.

       --verbose (-v)
              Display  informational  messages  and the full list of parameters and timers at the
              end of execution.

       --version (-V)
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_file (-o) [string]
              File to save modified dataset to. Default value ’'.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

       For further information, including relevant papers, citations,  and  theory,  For  further
       information,  including  relevant papers, citations, and theory, consult the documentation
       found at http://www.mlpack.org or included with your consult the  documentation  found  at
       http://www.mlpack.org  or  included  with  your  DISTRIBUTION  OF MLPACK.  DISTRIBUTION OF
       MLPACK.

                                                                     mlpack_pca(16 November 2017)