NAME

       mlpack_pca - principal components analysis

SYNOPSIS

        mlpack_pca -i string [-c string] [-d int] [-s bool] [-r double] [-V bool] [-o string] [-h -v]

DESCRIPTION

       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_file (-i)' parameter to specify the dataset to perform PCA on. A  desired
       new dimensionality can be specified with the ’--new_dimensionality (-d)' parameter, or the
       desired variance to retain can be specified with the '--var_to_retain (-r)' parameter.  If
       desired, the dataset can be scaled before running PCA with the '--scale (-s)' parameter.

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

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

       $   mlpack_pca   --input_file   data.csv   --new_dimensionality  5  --decomposition_method
       randomized --output_file data_mod.csv

REQUIRED INPUT OPTIONS

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

OPTIONAL INPUT OPTIONS

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

       --help (-h) [bool]
              Default help info.

       --info [string]
              Print help on a specific option. Default value ''.

       --new_dimensionality (-d) [int]
              Desired  dimensionality  of  output  dataset.  If 0, no dimensionality reduction is
              performed.  Default value 0.

       --scale (-s) [bool]
              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) [bool]
              Display informational messages and the full list of parameters and  timers  at  the
              end of execution.

       --version (-V) [bool]
              Display the version of mlpack.

OPTIONAL OUTPUT OPTIONS

       --output_file (-o) [string]
              Matrix to save modified dataset to.

ADDITIONAL INFORMATION

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